eccv local label descriptor for example based semantic image...
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ECCV
#1378ECCV
#1378
Local Label Descriptor for Example basedSemantic Image Labeling
Supplementary Material for ECCV submission
Paper ID 1378
In this supplementary document, we provide details of our tree constructionand additional experiment results.
A Tree Construction
We construct a random tree in the same way as [1], except that we make somechanges to adapt the procedure to our label histogram descriptors (instead ofraw label patches).
Specifically, we first collect the feature descriptor and label descriptor pairs(descriptor pairs), denoted by (gj ,qj), for all the patches in the training set,which form the root of one tree. We randomly select nh feature descriptor com-ponents to generate split hypotheses; each split hypothesis is a random thresholdgenerated within the range of feature descriptor values of the selected compo-nent. A random component in label descriptor will also be selected to evaluateall the nh split hypothesis. The split that results in the minimum entropy at theselected label descriptor component is selected as the final split to divide thisset (root) into two parts (nodes). Then we split each node recursively using thesame randomized procedure.
The splitting stops when the feature descriptors are similar enough. In theend, this recursive procedure generates a random binary tree whose leaf nodescontain a small set of descriptor pairs. If the label descriptors are also similar,we randomly select a descriptor pair as an exemplar. Otherwise, we cluster thedescriptor pairs based on label descriptors and randomly pick one descriptorpair from each cluster as exemplars. This will maintain the label diversity in leafnodes.
Note that in [1], raw label patches are used, which is equivalent to use labeldescriptors with label cell size 1. In the splitting procedure, we use nh = 20.
B A Synthetic Experiment on CamVid
The quality of candidate sets plays a critical role that affects the final perfor-mance of both the baseline algorithm and ours. In an extreme case, if a testingimage has a completely different visual appearance from the training images(e.g., training on street views but testing on desert scenes), neither algorithmswould perform well. To isolate the effect of candidate sets in evaluating ourmethod, we perform the following synthetic experiment.
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ECCV
#1378ECCV
#1378
2 ECCV-12 submission ID 1378
Setting Description Global Avg(Class) Avg(Pascal)
Baseline 85.55 64.00 55.12(1) Off, (2) On, (3) On 91.37 72.85 65.40(1) On, (2) On, (3) On 94.00 78.47 69.74
(1) Over-Segmentation (2) Continuous Optimization (3) Label DescriptorBaseline [1] (our implementation) = (1) Off, (2) Off, (3) Off
Table 1: Overall accuracy in the synthetic experiment (described in Section B).In this experiment, all methods use the same nearly ideal candidate sets. Con-tinuous optimization (convex relaxation) and label descriptor improve the per-formance noticeably compared to the baseline, in both overall correctness andaverage per-class scores. The low level over-segmentation further boosts the per-formance as pixels of similar colors are constrained to have the same label.
bycyclist luggage child text pedestrian sidewalk SUV vegetation
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baseline(1) off, (2) on, (3) on(1) on, (2) on, (3) on
(1) Over-Segmentation (2) Continuous Optimization (3) Label DescriptorBaseline [1] (our implementation) = (1) off, (2) off, (3) off
Fig. 1: Performance comparison on small object classes in the synthetic experi-ment (described in Section B). Combined with continuous optimization (convexrelaxation), our label descriptor better identifies small objects in the imagesby alleviating the misalignment problem. Using over-segmentation, our methodfurther improves over the baseline on the small object classes.
Fig. 2: Testing images used in Figure 3.
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ECCV
#1378ECCV
#1378
ECCV-12 submission ID 1378 3
Ground Truthfor Images in
Figure 2
Baseline
overall: 87.4
76.5 90.9 8.5 N/A 20.7
overall: 82.9
83.1 87.3 14.5 47.5 27.2
overall: 73.8
63.2 33.3 N/A 60.3 28.8
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overall: 93.9
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overall: 92.3
91.8 90.4 84.8 58.4 22.2
overall: 88.3
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overall: 96.1
93.6 94.7 53.6 N/A 19.8
overall: 94.4
91.4 93.5 88.6 65.5 46.7
overall: 93.7
81.5 55.9 N/A 94.4 51.0
Sidewalk Car Pedestrian Tree Column
(1) Over-Segmentation (2) Continuous Optimization (3) Label DescriptorBaseline [1] (our implementation) = (1) Off, (2) Off, (3) Off
Fig. 3: Three example results from the synthetic experiment (described in Sec-tion B). Overall, the performance of our method improves as more design choicesare applied. In particular, when all design choices are applied, our method oftenperforms the best. It is interesting to note that, although COLUMNs appear inthe baseline’s result for the third image (second row, third column), they are notin the correct orientation, because the baseline method uses raw label patchesfor label map inference and lacks the flexibility to handle label patch misalign-ment. Using label descriptors and over-segmentation, our method alleviates thisproblem and generates better results (third and fourth rows, third column).
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ECCV
#1378ECCV
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4 ECCV-12 submission ID 1378
For each training image, we generate a slightly rotated (5-degree) and scaled(a factor of 0.9) version as a testing image. For each patch in the testing (trans-formed) image, we collect its top nearest neighbors from the corresponding orig-inal (non-transformed) image to form the candidate set. The nearest neighborsare defined using the Euclidean distance between feature descriptors. Such can-didate sets are nearly optimal, subject to the label patch misalignment and thepossible feature descriptor matching failure, both of which are introduced by thegeometric transformation.
On such synthetic testing dataset (generated from the 377 training imagesin the CamVid dataset), we evaluate the baseline method and 3 design choicesof our method. The overall accuracy for different methods is shown in Table 1;the performance on small object classes is shown in Figure 1. Our methodsoutperform the baseline in almost every per-class accuracy, especially on smallobject classes. Several qualitative visual comparisons are provided in Figure 3.
Note that in this experiment, all methods use the same nearly ideal candidatesets. Therefore the performance difference should be attributed to algorithmdesign choices.
C Additional Real Experiments on CamVid
We provide additional experiments and visualization to further evaluate ouralgorithm design choices. We use all the 233 real testing images (including theimages at dusk) in the CamVid dataset and test on the 11 classes as in [1]. Tomeasure the impact of each design choice, we start from the baseline method,and turn on one or two design choices each time and evaluate their performance.
The overall accuracy for each combination of design choices is shown in Ta-ble 2. The per-class accuracy on images in daylight is shown in Figure 4 andFigure 5 for large and small object classes, respectively; images at dusk are ex-cluded in per-class accuracy evaluation because small objects are too similar tothe background, making them hard to recognize even for human vision. Severalqualitative visual comparisons are provided in Figure 7.
Setting Description Global Avg(Class) Avg(Pascal)
Baseline 59.40 28.38 20.09(1) On, (2) Off, (3) Off, (4) Off 62.07 29.33 21.50(1) On, (2) On, (3) On, (4) Off 66.25 32.54 24.76(1) On, (2) On, (3) On, (4) On 67.93 33.95 26.35
(1) Over-Segmentation (2) Continuous Optimization(3) Label Descriptor (4) Candidate Set EvolutionBaseline [1] (our implementation) = (1) Off, (2) Off, (3) Off, (4) Off
Table 2: Overall accuracy in the experiment in Section C. As each of our designchoices is turned on, all the scores increase.
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ECCV
#1378ECCV
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ECCV-12 submission ID 1378 5
Overall, the performance of our method improves as more design choices areapplied. In particular, when all design choices are applied, our method oftenperforms the best.
We also note that the accuracy for smaller object classes shown in Figure 5 ismuch lower than that for large object classes shown in Figure 4, which suggeststhat more research is definitely needed in this regard for this approach.
References
1. Kontschieder, P., Bulo, S., Bischof, H., Pelillo, M.: Structured class-labels in randomforests for semantic image labelling. In: ICCV. (2011)
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6 ECCV-12 submission ID 1378
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baseline(1) on, (2) off, (3) off, (4) off(1) on, (2) on, (3) on, (4) off(1) on, (2) on, (3) on, (4) on
(1) Over-Segmentation (2) Continuous Optimization(3) Label Descriptor (4) Candidate Set EvolutionBaseline [1] (our implementation) = (1) Off, (2) Off, (3) Off, (4) Off
Fig. 4: Results for the experiment in Section C. Per-class accuracy for the largestobject classes: ROAD, BUILDING, SKY, and TREE, which account for 29.7%,28.3%, 17.2% and 7.5% of the pixels in the testing images, respectively. Per-formance of all methods are comparable at the largest class ROAD; and ourmethods, especially when all design choices are ON, are generally better thanthe baseline on the other large classes BUILDING, SKY, and TREE. Note thatover-segmentation significantly improves the performance on SKY, because theSKY often appears as a region of uniform colors and it is relatively easy to gen-erate a good segment for SKY in the image. For TREE, it often contains morestructure details than the other three classes; as a result, combining label de-scriptor and continuous optimization (convex relaxation) shows a very noticeableimprovement on TREE.
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ECCV-12 submission ID 1378 7
sidewalk bicyclist sign fence pedestrian car column
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(1) Over-Segmentation (2) Continuous Optimization(3) Label Descriptor (4) Candidate Set EvolutionBaseline [1] (our implementation) = (1) Off, (2) Off, (3) Off, (4) Off
Fig. 5: Results for the experiment in Section C. Per-class accuracy for smallerobject classes. Compared with the baseline, combining continuous optimizationand local label descriptor noticeably increase the accuracy on smaller objectssuch as BICYCLIST, PEDESTRIAN, CAR, and COLUMN. The appearance ofSIDEWALK is often confused with ROAD; so the initial candidate sets can bewrong. Candidate set evolution helps to improve the accuracy for SIDEWALK.No methods can handle fence and sign. We suspect it is due to the lack of trainingdata on these classes.
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#1378ECCV
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8 ECCV-12 submission ID 1378
Fig. 6: Testing images and corresponding grounth truth labels in Figure 7.
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ECCV
#1378ECCV
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ECCV-12 submission ID 1378 9
Baseline
overall: 80.6
34.7 N/A N/A 0.7
overall: 82.3
35.5 56.1 N/A 9.2
overall: 86.4
18.8 N/A 66.0 7.9
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overall: 80.3
10.2 N/A N/A 0.0
overall: 80.0
12.9 59.6 N/A 6.4
overall: 61.4
1.5 N/A 73.1 3.5
(1) On(2) On(3) On(4) Off
overall: 89.0
58.6 N/A N/A 2.9
overall: 85.2
33.3 64.5 N/A 10.0
overall: 63.4
1.7 N/A 79.5 18.3
(1) On(2) On(3) On(4) On
overall: 92.6
92.1 N/A N/A 7.5
overall: 92.4
91.5 64.5 N/A 10.5
overall: 71.1
69.9 N/A 80.9 39.4
(1) Over Segmentation (2) Continuous Optimization(3) Label Descriptor (4) Candidate Set EvolutionBaseline [1] (our implementation) = (1) Off, (2) Off, (3) Off, (4) Off
Sidewalk Car Tree Column
Fig. 7: Three example results from the experiment in Section C. Overall, theperformance of our method improves as more design choices are applied. Inparticular, when all design choices are applied, our method often performs thebest.