wiamis2010 poster
DESCRIPTION
It is widely acknowledged that good performances of content-based image retrieval systems can be attained by adopting relevance feedback mechanisms. One of the main difficulties in exploiting relevance information is the availability of few relevant images, as users typically label a few dozen of images, the majority of them often being non-relevant to user’s needs. In order to boost the learning capabilities of relevance feedback techniques, this paper proposes the creation of points in the feature space which can be considered as representation of relevant images. The new points are generated taking into account not only the available relevant points in the feature space, but also the relative positions of non-relevant ones. This approach has been tested on a relevance feedback technique, based on the Nearest-Neighbor classification paradigm. Reported experiments show the effectiveness of the proposed technique relatively to precision and recall.TRANSCRIPT
The choice of all parameter is far from being a trivial task; theeffectiveness of the method depends from:
K-NEAREST NEIGHBORS DIRECTED SYNTHETIC IMAGES INJECTIONLuca Piras, Giorgio Giacinto
Dept. of Electrical and Electronic Engineering - University of Cagliari, Italy
GroupR APPattern Recognition and Applications Grouphttp://prag.diee.unica.it/pra/eng/home
[email protected] - [email protected]
WIAMIS 2010
After severaliterations
The system outputs the results
Compute the similarity betweenthe query image and theimages in the database
Compute the newquery using the
user's hints
Relevant images found atthe previous steps
The user marks the relevant and non-relevantimages for the relevance feedback
RelevantNon-relevant
Example-Imagechosen by a user to
perform a search in aDigital Library
DigitalLibrary
Relevance feedbackRelevance feedback techniques are employed to capture thesubjectivity of retrieval results and to refine the search. The user is askedto label the images as being relevant or not, and the similarity measureis modified accordingly: the images in the database are ranked interms of the distances from the nearest relevant and non-relevantimages.
Lack of relevant imagesOne of the most severe problems in exploiting relevance feedback inimage retrieval is the small number of images that the user considers asbeing “relevant” compared to the number of non-relevant imagesespecially during the first iterations.
I) Even in the case of very “cooperative” users, it is not feasible todisplay more than a few dozens of images to label.
II) If the database at hand is very large, then the number of imagesthat are relevant to the query can easily be very small compared tothe size of the database.
III) In a high dimensional feature space images with differentsemantic content can lie near each other.
K-Nearest Neighbors Directed Pattern InjectionWe propose to address the problem of small number of trainingpatterns by artificially increasing the number of relevant patterns inthe feature space. The basic idea underlying K-Nearest NeighborsDirected Pattern Injection consists in adding some artificial patternsgenerated in the feature space by taking into account the Krelevant images nearest to a reference point in the feature space.
Pi= X + ! "
1
K#
kk=1
K
$ X % Qk( )
k!n° Nearest NeighborsK ( )0,1N!
! Scaling factor
Experimental resultsThe Caltech-256 dataset obtained from the California Institute ofTechnology repository has been used. It consists of 30607 imagessubdivided into 257 semantic classes and the number of images perclass ranges from 80 to 827.
500 images have been randomly extracted from all of the 257 classes,and used as query. The top twenty nearest neighbors of each queryare returned. 9 relevance feedback iterations are performed.
relevanceNN
I( ) =I ! NN
nrI( )
I ! NNr
I( ) + I ! NNnr
I( )
NN
rI( ) resp., NN
nrI( )( ) nearest relevant (resp., non-relevant) image to
image I.
Our proposalIncreasing the number of “relevant” patterns used to train the system:
I) Creating new random artificial patterns by exploiting nearestneighbor information.II) Constraining these patterns in a region of the feature spacecontaining relevant images.
k-th relevant image nearest to XkQ
Reference relevant imageX
- Query- mean vector of all the relevant images (Mr)- each relevant images (Ar)- a point computed according to the BQS query movement technique (BQS)
( )2
1 2
1!
" "=
+
( )1 2
1!
" "=
+
( )2 2
1 2
1!
" "=
+
Bound of nearestrelevant image
Bound of farthestrelevant image
Number of pattern Pi :
N
R + P= n where n = 2, …, 5
The Tamura representation has been used (18-dimensional feature vector).
F =1
1
2 ! prec+
1
2 !recall
Bound of linearcombination
New artificialpattern