Object Browsing and Searching in A Camera Network using Graph Models

Zefeng Ni†, Jiejun Xu?, B. S. Manjunath†

Department of Electrical and Computer Engineering, University of California, Santa Barbara†

Department of Computer Science, University of California, Santa Barbara?

{zefengni, manj}@ece.ucsb.edu†, jiejun@cs.ucsb.edu?

Abstract

This paper proposes a novel system to assist human im-age analysts to effectively browse and search for objects in acamera network. In contrast to the existing approaches thatfocus on finding global trajectories across cameras, the pro-posed approach directly models the relationship among rawcamera observations. A graph model is proposed to repre-sent detected/tracked objects, their appearance and spatial-temporal relationships. In order to minimize communica-tion requirements, we assume that raw video is processedat camera nodes independently to compute object identi-ties and trajectories at video rate. However, this would re-sult in unreliable object locations and/or trajectories. Theproposed graph structure captures the uncertainty in thesecamera observations by effectively modeling their global re-lationships, and enables a human analyst to query, browseand search the data collected from the camera network. Anovel graph ranking framework is proposed for the searchand retrieval task, and the absorbing random walk algo-rithm is adapted to retrieve a representative and diverseset of video frames from the cameras in response to a userquery. Preliminary results on a wide area camera networkare presented. 1

1. Introduction

Wide area video surveillance requires the use of manycameras. Traditional centralized approaches to video anal-ysis do not scale well as the number of cameras in a net-work increase. In addition, recent technological advancesin imaging, embedded computing and communication, havemade it possible to consider decentralized processing. Insuch a set up, raw videos are analyzed at individual sen-sor nodes and information exchanged between cameras de-pending on application needs and priorities. At present,there are no good general strategies in such a network thatwould facilitate easy interaction between human image an-

1This work was supported by ONR grant # N00014-10-1-0478

alysts and the data collected/analyzed at the remote cameranodes. This work is an attempt to fulfill this critical need.

We consider a fixed camera network deployed over awide area (see Figure 1 and Figure 3). Raw videos arearchived at the remote camera nodes and each camera nodehas limited processing power for simple video analysis suchas motion detection and tracking. Given the bandwidthconstraint, there is no live video streaming to the distancecentral node where human analysts are. We envision thefollowing two application scenarios for the interaction be-tween the user and the camera network:

• Browsing(see Figure 5 and 7): A user instantiates theinteraction with the network by specifying regions onthe image plane (cameras, time intervals) of interest.An example query could be “FIND object instances re-lated to regionA FROM camera 1 OR regionB FROMcamera 4 between time 9:30am and 9:35am”. For eachquery, the system needs to provide a “smart summa-rization” with an overview of network activities thatsatisfy the query criterion. The reason for the “sum-marization” is to reduce communication cost when ac-cessing the remote videos and alleviate human effortswhen interpreting the query results.• Searching(see Figure 9): With the results from the

previous scenario, the user could then identify specificobjects of interest to initiate further searching for thesame or related objects. An example query here couldbe “FIND all objects related to the object instance atregion C FROM camera 1 at time 9:32:41.3am”. Aspecial case for this query scenario is the classic prob-lem of object re-identification, i.e. find the instancesof the same object in all camera views.

Designing a system to address such queries is an interest-ing and challenging problem, and is the primary motivationfor the proposed work. One way to address this problem isto provide human users with an high-level interface, suchas dynamic global scene visualization [1, 6]. To achievethis, prior research has focused on methods that can detectand track all observed objects across the entire camera net-

978-1-4673-1612-5/12/$31.00 ©2012 IEEE 7

Human image

analyst

Query (e.g., finding object instances in camera 1 and 4 between time 9:40am and 9:50am)

Real-time abstracted observations from individual cameras(e.g., object 402 time 9:03:11 by camera 3)

Request cameras to send particular frame for human (e.g., Frame 101 from Camera 3 and frame 300 from camera 4)...

Query results

....

....

Camera 1 at Time 9:44:55am

Camera 2 at Time

9:45:05amCamera 4 at

Time 9:45:26am

Model the relationships of observations from all cameras

with a graph model

Rank all observations based on user query with graph ranking

(e.g., absorbing random walk)

....

Figure 1. The proposed system to facilitate human image analysts to efficiently browse and search objects in a camera network

work [7, 8, 10]. While this could be an ultimate goal foran ideal surveillance system, it is a difficult task to achievewith existing state of the art in computer vision. For exam-ple, with limited on-board processing and low-quality im-age sensors at remote camera nodes, it would be difficult todetect and track objects in a consistent manner.

In this work, instead of trying to find global trajectoriesfor every object visible in the network, we propose to com-pute representative raw video frames (snap shots) from in-dividual cameras. These frames are likely to contain eventsor objects of interest (see Figure 1) requested in the userquery. In particular, the proposed system acts as an inter-mediate agent between distributed camera nodes and humanimage analysts, and provides recommendations to the userwith a concise and authoritative set of frames captured bythe camera network. The goal is to help the image analyststo browse, search and identify objects of interest by givinga canonical overview of the entire set of visual observationsin the network.

The proposed system contains two essential parts: 1)

real-time object detection and tracking at the remote cam-era nodes; and 2) modeling of relationships among cam-era observations with a graph at a central node. The keycontributions of this paper include constructing a time-evolving graph based on remote camera observations andserving various user queries by ranking graph nodes (rawvideo frames with observations) and recommending thehigh-ranked frames to user. To prevent redundant itemsfrom receiving high ranks, we utilize the absorbing randomwalk [18] to improve searching diversity and present theuser with a diverse and representative visual summariza-tion. To the best of our knowledge, this proposed systemis the first attempt to allow user interaction with distributedcamera network by utilizing graph modeling and rankingto facilitate effective object browsing and searching. Thissystem is successfully demonstrated with an 11-camera out-door network.

The rest of the paper is organized as follows. Sec-tion 2 describes related works on object re-identificationand graph-based method for visual searching. Section 3

8

describes the methodology in details. Finally, Section 4presents experimental results on a real 11-camera outdoorcamera network and Section 5 concludes this paper.

2. Related Work

The proposed system is related to the problem of ob-ject reacquisition or re-identification in multiple cameras.In [1, 6], similar systems with distributed cameras are pro-posed, with a server collecting camera observations and as-signing unique global object ID based on object’s estimatedlocation and/or color appearance. To deal with appearancevariations across views, much work has been done on find-ing the best matching criterion, for example the joint mo-tion and appearance model in [7], low-dimension subspacelearning of brightness transfer functions in [8], symmetry-driven accumulation of local features in [5], probabilisticrelative distance comparison in [17], and the shared set ofhaar-feature in [14]. All these methods share one commonproperty, that is the pair-wise comparison of measurementsfrom different camera views. This way of direct comparisonmight suffer when the measurements (object detection andtracking) from the individual cameras are noisy. A moreeffective way of relating observations from different cam-eras is to treat them collectively, instead of doing pair-wisesimilarity comparison, such as the method in [10], whichfinds optimum paths (maximum a posterior estimates) overall camera observations. However, their proposed solutionof linear programming still requires the perfect detectionand tracking from individual cameras. In this paper, wepropose to utilize a graph to represent the underlying rela-tionships among camera observations and cast the problemof user interaction as an unified graph ranking problem byidentifying representative snap shots that could contain theobservations requested by the user query.

The proposed system adapts concepts from content-based image retrieval (CBIR), especially graph modeling inlarge-scale image databases. In [9] a visual ranking algo-rithm was proposed to apply graph-based PageRank for im-age search. However, visual features alone might not be suf-ficient to convey the semantics in the images. Researchersin the CBIR community have tried to exploit multiple in-formation cues to alleviate this problem. For example, agraph framework was utilized in [16] to fuse informationfrom multiple sources (e.g. image feature and text annota-tion). The utilization of graph model introduces structuresto the data to capture their global inter-relationship and ex-ploit the mutual reinforcement among different modalities.Similar ideas were used in [12, 15]. In summary, graphmodeling has been proven to be an efficient method to com-bine multiple cues, especially for large databases.

3. Proposed MethodFigure 1 gives an overview for the proposed system. As-

suming a network of N distributed static cameras with em-bedded storage and computing power, each camera node in-dependently detects and tracks moving objects in real time.For each frame with detected objects, the camera sends anabstracted record, including object’s spatial, temporal andappearance information, to a central node. At the centralnode, a time-evolving graph is incrementally built to modelthe relationships among the camera observations based onthe received records.

Given a user’s query, e.g., “FIND observations related toregion A of camera 2 between time t1 and t2”, the centralnode performs ranking on the graph to identify a represen-tative and concise set of frames and then requests the re-mote cameras to deliver the corresponding snap shots overthe network. In this way, the system avoids the need of anyreal-time video streaming, which could be prohibitively ex-pensive.

3.1. Real-time Distributed Detection and Tracking

At each camera node, the system detects interesting ob-jects and tracks them on the image plane. Assuming a staticcamera network, objects can be detected by modeling back-ground and identifying moving foreground. In particular,foreground pixels are identified using background subtrac-tion [11]. Connected foreground pixels are combined to-gether to form foreground blobs, which are then tracked bya mean shift algorithm [3]. In the current set up, each objectis represented with a rectangular blob. To address the prob-lem of scale variations, we utilize the the general mean-shiftblob tracking algorithm proposed in [2].

For each tracked object, a unique object ID is assigned 2.For each frame processed by the camera, a record is gener-ated for each detected/tracked object and sent to the centralnode over the network. Each observation record includesinformation such as camera ID, time, object’s position onthe image plane and a 16-bin Hue histogram as appearancerepresentation.

3.2. Modeling Camera Observations with a Graph

Given a user query, the system aims to find the obser-vations with the following two properties, centrality (i.e.,representative ones which are closely related to the queryand many other observations and hence considered impor-tant) and diversity (i.e., covering as many distinct groups aspossible). In a browsing scenario, there is no live video forthe user to monitor in real time. The system should providea smart “summarization” from all the cameras. A framewith more detected objects is considered more important to

2Unique for the particular camera but not across cameras, therefore nocross camera collaboration/communication is required.

9

the human analysts. Similarly, an object observed by mul-tiple cameras over a longer duration is more important thanan object just appearing in a single camera. On the otherhand, in a searching scenario, a user might be interestedin a particular object(s). Instead of showing near-identicalobservations, it would be more interesting to display the ob-servations with different properties, e.g., different visual ap-pearance and from different cameras etc.

With a graph framework, we can easily address the aboverequirements utilizing effective graph ranking algorithms,e.g., absorbing random walks [18] and manifold rankingwith stopping points [19]. Further more, the graph frame-work presents a principled formulation to answer differentqueries. In such a graph framework, individual camera ob-servations (i.e., frames with detected objects) form the ver-tices V in a simple graph G(V,W). The weight matrix Wdefines the strength of connectivity between camera obser-vations (e.g., the same object at different views). Note thatthe graphG is built at the central server incrementally as thenew records are received in real time from the cameras.

Time delay Td

between two blocks

Figure 2. Spatial-temporal topology across cameras

In our current implementation, W is estimated ac-cording to objects’ visual appearance and spatial-temporaltopology in the camera network. Given two vertices Xi andXj , their edge weight Wij is calculated using Algorithm 1.If the two vertices are from the same camera, Wij is set tokω where k is the number of common objects (records withsame local object ID) in Xi and Xj and ω here is a con-stant. If frame Xi and Xj are from different cameras, wefirst check whether it is likely that the two observations arecaused by the same object based on the network’s spatial-temporal topology (see Figure 2).

To model the spatial-temporal topology across cameras,the image plane from each camera view is divided into 8x6blocks. We assume the time delay Td for an object to travelacross any two blocks follows a Gaussian distribution with

known mean µ and variance δ2. With this topology modeland two observation records Ri and Rj , from Xi and Xj

respectively, we can calculate PST (Ri, Rj), the likelihoodthatRi andRj belongs to the same object, based on the timedelay betweenBlock(Ri) andBlock(Rj). If PST (Ri, Rj)is larger than a threshold 3, the weight Wij is increasedby pA(Ri, Rj) ∗ pST (Ri, Rj), where pA(Ri, Rj) modelstwo record’s similarity in visual appearance (correlation be-tween the Hue-histograms of record Ri and Rj).

Algorithm 1 Weight computation between two verticesInput: Two vertices Xi and Xj in the graphOutput: Edge weight between Wij between Xi and Xj

1: Initialization: Wij = 0, i.e., no connection.2: for Each each object record Ri in Xi do3: for Each each object record Rj in Xj do4: if Ri and Rj are from the same camera and share

the same object ID then5: Wij = Wij + ω6: else7: if Ri and Rj are from different cameras and

pST (Ri, Rj) > Threshold then8: Wij = Wij + pA(Ri, Rj) ∗ pST (Ri, Rj)9: end if

10: end if11: end for12: end for

3.3. Query Serving with Graph-based Ranking

With the graph modeling of camera observations, we canutilize off-the-shelf graph ranking methods to answer dif-ferent user queries. Among them, VisualRank [9] is prob-ably the most related to our scenario. Essentially a simi-larity graph is constructed based on image visual similar-ity, and the PageRank algorithm [13] is applied to re-rankthe initial text-based searching results. However PageRankdoes not ensure diversity at all, i.e. if two images are bothvery similar to many other images, they will have similar(high) ranks. Thus redundant information is being kept.In order to deliver more diverse ranking results, recentlymethods such as absorbing random walks [18], decayedDivRank [4], and manifold ranking with stop points [19]have been proposed. These methods perform quite simi-larly, this paper adapts the absorbing random walk approachsince it is easy to implement. The main idea is to let ahigh ranked node to transform into an “absorbing” stateduring the random walk on the graph. This node will then“drag down” the importance value of other similar unrankednodes, thus encouraging diversity. The algorithm consists

3This threshold helps to remove edges with negligible weights, whichsimplifies the graph model and improves ranking speed significantly withlittle effect on the final results.

10

of two parts. The first part is to find the overall top rankednode. Assuming an n × n weight/affinity matrix W, a rawtransition matrix P̃ is defined by row-normalizing W, i.e.P̃ij = Wi,j/

∑nk=1 Wik, such that P̃ij is the probability

that the random walker moves from vertex i to j. Then ateleporting random walk P is defined by adding each rowwith the user-supplied initial preference vector r,

P = λP̃+ (1− λ)erT, (1)

where e is an all-1 vector. The r is determined accordinglyto the particular query scenario. The final ranking vectorπ is the stationary distribution of the random walk, i.e., thesolution for equation π = PTπ. The vertex with the largeststationary probability is the overall top ranked observation,i.e.,g1 = argmaxni=1πi

The second part of the absorbing random walk is a seriesof ranking iterations to pick the remaining vertices in thegraph. Suppose a group of top-ranked vertices G = {gi}have been selected, they are turned into absorbing statesby setting Pgg = 1 and Pgi = 0, ∀i 6= g, which is es-sentially adding a self-edge to those vertices and makingthem into sinking/stopping states. If we arrange verticessuch that ranked ones are listed before unranked ones, wemodify transition matrix P to

P =

[IG 0R Q

]. (2)

Here IG is the identify matrix on G. Submatrices R and Qcorrespond to the rows of unranked items from (1).

Based on the above matrix, we can compute the expectednumber of visits to each remaining nodes before reachingany absorption by v = (NTe)/(n−|G|), where N is knownas the fundamental matrix: (I−Q)−1. Again, we can selectthe vertex with the largest expected number of visits as thenext item in ranking: g|G|+1 = argmaxn|G|+1vi. The mainsteps to compute the diverse ranked list is summarized inAlgorithm 2.

Algorithm 2 Serve user query by ranking camera observa-tions with absorbing random walkInput: Graph weight matrix W and preference vector r.Output: Top-ranked vertices {g1, g2, g3, . . .}.

1: Compute the initial transition matrix P from (1).2: Compute stationary distribution π.3: Pick the top ranked item g1 = argmaxiπi.4: while Need to look for enough high ranked vertices do5: Convert ranked vertices into absorbing states (2).6: Compute the expected number of visits for all re-

maining vertices before reaching any absorption.7: Pick the next vertex g|G|+1 = argmaxiπi.8: end while

The preference vector r in (1) is a n-dim vector repre-senting the user query. The entries of the vector are mostlyzeros, except for the ones that correspond directly to the ver-tices (i.e., camera observations) carrying initial query inten-tion. For instance, suppose an image analyst is interestedin objects related to “region B of camera c between timet1 and t2”. The system will first identify all frames withrecords that match this criteria and then mark the corre-sponding m vertices {Gq} as the query vertices. Then, auniform score is given the vertices in this query set {Gq},i.e., ri = 1/m if i ∈ {Gq}, and ri = 0 otherwise. Here wecan consider r as an initial ranking vector that kick-startsthe absorbing random walk. For the searching query, it ismore straightforward. For example, to search for a particu-lar object instance at time t of camera 3, which correspondsthe vertex j in the graph, preference vector is set as rj = 1with all other entries as 0.

4. Experiments

1

2

7

10

3 4

56

8

911

Central node

Figure 3. Experimental setup: an outdoor network with 11 cam-era nodes observing bike paths (shown in green line, the area isapproximately 600 meters in width and length).

To demonstrate the proposed system, an outdoor net-work of 11 cameras is deployed along bike paths in an ur-ban environment (see Figure 3). In particular, our “smartcamera node” consists of two parts: a Cisco WVC2300wireless-G Internet video camera and a nearby dedicatedcomputer. The local computer achieves and processes thelive streamed video (640x480, about 20fps) from the Ciscocamera. The computer and the wireless camera togethersimulate a distributed smart camera node in a camera net-work. These “smart camera nodes” communicate with adistance central server node, where the human user locates.

11

C8 C9 C10 C11

Figure 4. Example graph weight matrix W with observations from cam-era 8-11 in 60 seconds. Brightness indicates edge weight.

Time of Interest: 9:39am to 9:42am

C8 C9

Figure 5. Browsing scenario 1 with regions of interest indicated by therectangles in camera C8 and C9.

Time of Interest: 9:44am to 9:50am

C2 C3Figure 6. Browsing scenario 2 with regions of interest indicated by therectangles in camera C2 and C3.

It is a challenging task to reliably detect and track cyclistsand pedestrians observed in the scene, especially with lowquality video sent from the wireless cameras. Hence this ex-perimental set up serves as a good test bed for the proposedsystem.

Due to the nature of the problem, there is no off-the-shelf metric to perform large-scale quantitative evaluation.In this paper, we demonstrate the effectiveness of the sys-tem with a few application scenarios. Figure 5 and 6 showtwo “browsing” examples and Figure 7 and 8 show the cor-responding top ranked camera observations. The systemallows regions of interest from any set of cameras, whichsimply specify the preference vector r for the absorbing

random walk algorithm. The anticipated result is that topranked frames should contain majority of the objects (di-versity) related to the browsing query (i.e., the object haspassed through the regions of interest of all the queried cam-eras within the specified time). This does not mean the re-turned frames must be from these regions of interests, asother frames might contain the same objects with more in-formation and hence more representative (centrality). Thefollowing table shows the error (wrong objects) and recall(number of matched objects which have been identified) forthe two scenarios.

Browsing 1 (Top 10) Browsing 2 (Top 15)Recall 10 out of 10 objects 17 out of 22 objectsError 1 out 10 frames 0 out 15 frames

With results from browsing, a user can further initiatesearching of a particular object instance. Figure 9 shows thesearching result when querying with the 5th ranked frame ofFigure 7 (starred). Figure 10 shows the results when search-ing for the 11th ranked frame of Figure 8 (starred). For bothcases, the top ranked frames contain a diverse set of objectsthat is spatially or temporally close to the query object. Col-lectively, these frames tell a summarized “story” for the ob-ject of interest. The red cyclist in Figure 9 travels along thebike path alone all the time, thus the system returns snap-shots of him passing different camera views. In addition,the top ranked frames contain other cyclists who are tempo-rally nearby. In Figure 10, the pink cyclist travels along thebike path while occasionally passed by other cyclists. Asa result, the system finds those frames which contain othercyclists which are spatially nearby.

5. DiscussionsThis paper proposes a novel system to assist human im-

age analysts to effectively browse and search for objects ina large distributed camera network for visual surveillance.In particular, the proposed approach directly models the re-lationship among raw camera observations with a graph.All frames with detected/tracked objects are treated as ver-tices in a graph, with edges determined by spatial-temporaltopology and visual appearance. With the proposed ap-proach, reliable detection and tracking from local camerasis not required, as there is no need for cross camera ob-ject association. The graph structure naturally captures theglobal relationship of camera observations, and enables thesystem to answer various human queries through a unifiedranking framework. The system utilizes absorbing randomwalk algorithm to retrieve a representative and diverse setof video frames based on the human queries. The effective-ness of the system is demonstrated with a 11-node outdoorcamera network. For future work, we would like to uti-lize the similar graph model here for other applications suchas event recognitions by clustering graph vertices. In addi-

12

9:39:18.2amC8

9:39:27.8amC8

9:41:00.8amC8 9:38:55.6am C11 9:42:08.5am C6

9:39:42.6am C8 9:39:09am C8 9:41:46am C7 C8 C99:40:18am 9:41:07.1am

P1

P2 P3

P4

P5P6

P7P8

P5

P9

P10

P6

Figure 7. Top 10 ranked frames for browsing scenario 1 (Decreasing order: left to right, top to down. The green ellipses are the blobs detected by theremote camera nodes). There are a total of 10 distinct objects satisfying the criterion in Figure 5. All of them have been identified (labeled in yellow). The8th ranked frame is a “false positive” (it has not passed the queried regions within the specified time interval).

C39:39:27.8am

9:47:04.1am C29:46:27am 9:46:13.8am C5 C2

9:48:58.5am C3 9:44:02.7am C2 9:49:41.5am C3 C4 C39:46:15.2am 9:48:18.2am

C39:49:36.6am 9:47:06.3am

9:43:12.9am C2 C39:48:18.6am 9:45:52.6am C6 9:47:58.6am C3 9:45:06.3am C2

P1

P2

P3P4

P5P6

P7P8

P10

P9

P11

P12 P13 P14

P14

P6P15 P14

P17

P16

Figure 8. Top 15 ranked frames for browsing scenario 2 (Decreasing order: left to right, top to down. The green ellipses are the blobs detected by theremote camera nodes). There are a total of 22 distinct objects satisfying the criterion in Figure 6. 17 of them have been identified (labeled in yellow).

tion, we plan to prepare manually labeled object trajectoriesto facilitate large-scale quantitative performance evaluation.The presented data set in this paper will also be released tothe research community in the near future.

References[1] R. Collins, A. Lipton, H. Fujiyoshi, and T. Kanade. Algo-

rithms for cooperative multisensor surveillance. Proceedingsof the IEEE, 89(10):1456–1477, 2001. 1, 3

[2] R. T. Collins. Mean-shift blob tracking through scale space.In IEEE Conference on Computer Vision and Pattern Recog-nition, 2003. 3

[3] D. Comaniciu and P. Meer. Mean shift: a robust approachtoward feature space analysis. IEEE Transactions on Pattern

Analysis and Machine Intelligence, 24(4):603–619, 2002. 3[4] P. Du, J. Guo, and X.-Q. Cheng. Decayed divrank : Captur-

ing relevance, diversity and prestige in information networks.In International ACM SIGIR Conference on Research andDevelopment in Information Retrieval, pages 1239–1240,2011. 4

[5] M. Farenzena, L. Bazzani, A. Perina, V. Murino, andM. Cristani. Person re-identification by symmetry-driven ac-cumulation of local features. In IEEE Conference on Com-puter Vision and Pattern Recognition, pages 2360–2367,2010. 3

[6] O. Javed, Z. Rasheed, O. Alatas, and M. Shah. Knightˆm:A real time surveillance system for multiple overlapping andnon-overlapping cameras. In IEEE Conference on Multi me-dia and Expo, pages 6–9, 2003. 1, 3

13

9:41:48.7am C6

9:41:45.1am 9:41:07.1am C9 C6C7 9:41:28.1am

C8

9:41:13.3am9:41:41.5am C11

9:41:07.3am C9

9:41:37.8am C89:41:48.9am C9

C109:41:51am

Figure 9. Results when searching for object P6 from browsing scenario 1 (the starred frame in Figure 7 as the query frame; 2nd to 11th ranked frames aredisplayed here. Top ranked frame is omitted as it is the same as the query frame.

9:46:59.5am

9:46:15.3am 9:45:46am C5 C6C4 9:46:40.9am

C39:45:26.7am9:46:45.4am C7

9:45:52.7am C7

9:41:37.8amC2

9:47:06.2am C9

C59:46:15.2am

C2

Figure 10. Results when searching for object P6 from browsing scenario 2 (the starred frame in Figure 8 as the query frame; 2nd to 11th ranked frames aredisplayed here. Top ranked frame is omitted as it is the same as the query frame).

[7] O. Javed, Z. Rasheed, K. Shafique, and M. Shah. Trackingacross multiple cameras with disjoint views. In InternationalConference on Computer Vision, pages 952–960. IEEE Com-puter Society, 2003. 2, 3

[8] O. Javed, K. Shafique, and M. Shah. Appearance model-ing for tracking in multiple non-overlapping cameras. InIEEE Conference on Computer Vision and Pattern Recog-nition, pages 26–33, 2005. 2, 3

[9] Y. Jing and S. Baluja. Visualrank: Applying pagerank tolarge-scale image search. IEEE Transaction Pattern Analysisand Machine Intelligence, 30(11):1877–1890, 2008. 3, 4

[10] V. Kettnaker and R. Zabih. Bayesian multi-camera surveil-lance. In IEEE Conference on Computer Vision and PatternRecognition, pages 252–259, 1999. 2, 3

[11] L. Li, W. Huang, I. Y. H. Gu, and Q. Tian. An improvedadaptive background mixture model for real-time trackingwith shadow detection. In ACM International Conference onMultimedia, pages 2–10, 2003. 3

[12] E. Moxley, T. Mei, and B. S. Manjunath. Video annota-tion through search and graph reinforcement mining. IEEETransactions on Multimedia, 12(3):184–193, 2010. 3

[13] L. Page, S. Brin, R. Motwani, and T. Winograd. The pager-ank citation ranking: Bringing order to the web. TechnicalReport 1999-66, Stanford InfoLab, November 1999. 4

[14] R. Rios Cabrera, T. Tuytelaars, and L. Van Gool. Efficientmulti-camera detection, tracking, and identification using ashared set of haar-features. In IEEE Conference on Com-puter Vision and Pattern Recognition, pages 65–71, 2011. 3

[15] H.-K. Tan and C.-W. Ngo. Fusing heterogeneous modali-ties for video and image re-ranking. In Proceedings of the1st ACM International Conference on Multimedia Retrieval,pages 1–8, 2011. 3

[16] H. Tong, J. He, M. Li, C. Zhang, and W.-Y. Ma. Graph basedmulti-modality learning. In ACM International Conferenceon Multimedia, 2005. 3

[17] W.-S. Zheng, S. Gong, and T. Xiang. Person re-identificationby probabilistic relative distance comparison. In IEEE Con-ference on Computer Vision and Pattern Recognition, pages649–656, 2011. 3

[18] X. Zhu, A. B. Goldberg, J. Van Gael, and V. G. Andrzejew-ski. Improving diversity in ranking using absorbing randomwalks. In The Annual Conference of the North AmericanChapter of the Association for Computational Linguistics,pages 97–104, 2007. 2, 4

[19] X. Zhu, J. Guo, X. Cheng, P. Du, and H.-W. Shen. A unifiedframework for recommending diverse and relevant queries.In International conference on World Wide Web, pages 37–46, 2011. 4

14