Object and Action Detection from a Single Example

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1 Object and Action Detection from a Single Example Peyman Milanfar* EE Department University of California, Santa Cruz *Joint work with Hae Jong Seo AFOSR Program Review, June 4-5, 29

2 Take a look at this:

3 See it here?

4 How about here?

5 Or here?

6 Single Example, No Training! (Most) people can find the Dragon Fruit from one look. Even if they ve never seen it before.

7 Outline I. Motivation II. III. Overview Object Detection IV. Action Detection V. Conclusion and Future work

8 Fundamental Problems in Machine Vision Develop a unified framework that can robustly detect objects/actions of interest within images/videos without training query target image query target video 1) Whether objects (actions) are present or not, 2) How many objects (actions)? 3) Where are they located?

9 Challenges in Detection Objects Actions Besides, Contexts: Degradation: 1) different clothes, 2) different illumination, Medical imaging Underwater Raindrop Noise 3) different background 4) action speed Blur

10 Outline I. Motivation II. III. System Overview Object Detection IV. Action Detection V. Conclusion and Future work

11 Object Detection using Local Regression Kernels Local Steering Kernels as Descriptors Using a single example Resemblance Map Detected Similar Objects Query

12 Query Target Object Detection System Overview -.1 stage 1 Compute local steering kernels Descriptors ) Significance.5 Tests.1 3) Non-maxima Suppression Final result PCA stage 2 stage 3 1Image Feature 2Image Feature.1 1Image Feature 3Image 4Template Feature Feature Image 1Image compute feature images Image Feature.5 3Image Feature.1.2 2Image Feature.1 1Image Feature 4Image Feature -.5 3Image Feature -.1 4Template 2Image Feature Feature 4Image Feature 3Image Feature.1 1) Resemblance Map (RM).2 using Matrix Cosine Similarity 4Image Feature Image Feature 2Image Feature Image Feature 2Image Feature 3Image Feature 4Image Feature 3Image Feature 4Image Feature 1Image Fea 2Image Fea 3Image Fea.2 4Image Fea H. Seo and P. Milanfar, Training-free, Generic Object Detection using Locally Adaptive Regression Kernels, Accepted for publication in IEEE Transactions on Pattern Analysis and Machine Intelligence

13 Stage 1: Calculation of Local Descriptors SVD

14 Robustness of LSK Descriptors Original Brightness Contrast WGN image change change sigma =

15 System Overview.1 : Stage 2 1Image Feature Query Target stage 1 Compute local steering kernels ) Significance.5 Tests.1 3) Non-maxima Suppression Final result PCA stage 2 compute feature images stage 3.1 1Image Fea.2 2Image Feature Image Feature Image Fea.2 1Image Feature Image 4Template Feature Feature Image 3Image Fea Image 2Image Feature Image Feature Image Feature.5 3Image Feature Image Feature Image Feature Image Feature Image Feature Image Feature Image Feature Image Feature.1 1) Resemblance Map (RM).2 using Matrix Cosine Similarity Image Feature Image Fea -.2 3Image Feature 2Image Feature Image Feature 3Image Feature Image Feature

16 Energy Stage 2: Feature Extraction from Descriptors Densely collected Vectorization Descriptors Apply PCA to for dimensionality reduction Retain the d largest principal components Project and onto d Eigenvalue rank

17 Stage 2: Salient features after PCA LSK Object: Helicopter Query 1Image Target Feature 1 st eigenvector 2 nd eigenvector 3 rd eigenvector 1Image Feature 1Image Feature 1Image Feature 2Image Feature 2Image Feature 2Image Feature 2Image Feature 3Image Feature 3Image Feature 3Image Feature 3Image Feature 1Image Feature 1Image Feature 1Image Feature 1Image Feature 2Image Feature 2Image Feature 2Image Feature 2Image Feature 3Image Feature 3Image Feature 3Image Feature 3Image Feature 4 th eigenvector 4Image Feature 4Image Feature 4Image Feature 4Image Feature 4Image Feature 4Image Feature 4Image Feature Eigenvectors 4Image Feature Query features Target features 11

18 Stage 2: Salient features after PCA LSK Object: Car Query Target st eigenvector nd.2.4 eigenvector rd.2.4 eigenvector th.2.4 eigenvector Eigenvectors Query features Target features 11

19 Image Feature.2 System Overview.1 : Stage Query Target.1 1Image Fea.2 2Image Feature Image Feature Image Fea.2 1Image Feature stage 1 stage 2 3Image 4Template Feature Feature Image 3Image Fea Image.2.1 PCA -.5 2Image Feature Image Feature Image Fea Image Feature Image Feature.5 3Image Feature.1.1 2Image Feature Image Feature Image Feature Compute local steering kernels ) Significance Tests.5.1 3) Non-maxima Suppression -.5 Final result compute feature images stage Image Feature 3Image Feature.1.5 4Image Feature Image Feature Image Feature 4Image Feature Image Feature 3Image Feature.1 1) Resemblance Map (RM).2 using Matrix Cosine Similarity Image Feature

20 Stage 3: Finding similarity between features Target image is divided into a set of overlapping patches Query Target Query Target

21 Stage 3: Correlation based Metric The vector cosine similarity Query Target patch Inner product between two normalized vectors Measures angle while discarding the magnitude

22 Stage 3: Correlation based Metric The vector cosine similarity Query Target patch Inner product between two normalized vectors Measures angle while discarding the magnitude

23 Stage 3: Matrix Cosine Similarity What about a set of vectors? Matrix Cosine Similarity Frobenius Inner product between normalized matrices Query Target patch

24 Stage 3: Matrix Cosine Similarity What about a set of vectors? Matrix Cosine Similarity Frobenius Inner product between normalized matrices Query Target patch A weighted sum of the column-wise vector cosine similarities We can prove optimality of this approach in a naïve Bayes sense.

25 Stage 3: Generate Resemblance Map Resemblance Map (RM) Describes the proportion of variance in common between two features Lawley-Hotelling Trace statistic

26 Stage 3: Non-parametric Significance Tests 1. Is any sufficiently similar object present? i.e., 2. How many objects of interest are present? so that ~ 5 % of variance in common probability.4 Empirical PDF % confidence level Significance level

27 Experimental Results Query Targets Dataset from Weizmann Inst.

28 Experimental Results query query target:1 target target:3 target

29 Experimental Results query target target:2 target:1

30 Experimental Results query target:3 target target:1 target target:2 target 25

31 Experimental Results query Higher resemblance target target:1 target target:2 Lower resemblance

32 Detection rate Experimental Results Weizmann Inst. Object Test Set CIE L*a*b* channel Luminance channel only SIFT descriptor [1999] Shape Context [21] GLOH [25] x 1-4 Detection rate = TP/(TP+FN) False positive rate = FP/(FP+TN) False positive rate

33 Experimental Results The MIT-CMU Face Test Set Query

34 Detection rate Experimental Results 1 The MIT-CMU Face Test Set ROC curve x 1-4 False positive rate 36

35 Gallery Set:1 subjects x 25 different conditions Query Q

36 Gallery Set:1 subjects x 25 different conditions Query Q

37 query target output query target output

38 query target output query target output

39 Outline I. Motivation II. III. System Overview Object Detection IV. Action Detection V. Conclusion and Future work

40 Action Detection System Overview Query stage 1 stage 2 PCA Target Compute space-time local steering kernels compute feature volumes No Motion Estimation No Segmentation No Learning No Prior Information 2) Significance Tests 3) Non-maxima Suppression Final result Frame:256 stage 3 1) Resemblance Map (RM) using Matrix Cosine Similarity H. Seo and P. Milanfar, Generic Action Recognition from a Single Example, Submitted to International Journal of Computer Vision (IJCV), March 29

41 Stage 1: Space Time Descriptors : 3x3 local covariance matrix : space-time coordinates First frame Key frame Last frame 38

42 Experimental Results Shechtman s action test set (Beach walk) Query Typical run time for target (5 frames of 144 x 192) and query (13 frames of 9 x 11) : a little over 1 minute

43 Experimental Results (Multiple Actions) Multiple queries Automatic cropping Very Confusing Moving to the Left Very Confusing Very Confusing Very Confusing Moving to the Left Very Confusing Very Confusing Very Confusing Moving to the Left Very Confusing Very Confusing Very Confusing Moving to the Left Very Confusing Very Confusing Very Confusing Moving to the Left Very Confusing Very Confusing

44 Action Recognition Query Automatic cropping of a short action clip (25 frames) Action Category 1 5 Action Detection 2 6 Scoring Rank least similar most similar

45 Action Classification Performance Average confusion matrices Classification rate: 96 % Classification rate: % Bend Jack Jump box hclp Pjump Run Side Skip Walk hwav jog run Wave1 Wave walk (Weizmann dataset) (KTH dataset) 9 video sequences 6 video sequences Classification rate = 1 (# of miss classification) / (total # of sequences) Evaluation setting: Leave-one-out Classify each testing video as one of the predefined classes by 3-NN (nearest neighbor)

46 Action Classification Performance Comparison with state-of-the art methods (KTH dataset) Our Approach (1-NN) Our Approach (2-NN) Our Approach (3-NN) 89% 93% 95.66% Our Approach (3-NN) 95.66% Kim et al. (28) 95.33% Ali et al.(28) 87.7% Dollar et al. (25) 81.17% Ning et al. (28) 92.31% Niebles et al. (28) 81.5% Wong et al. (27) 71.16% Classification rate = 1 (# of miss classification) / (total # of sequences) We outperform all the state-of-the art methods on KTH dataset.

47 Publications H. Seo and P. Milanfar, Training-free, Generic Object Detection using Locally Adaptive Regression Kernels, Accepted for publication in IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 H. Seo and P. Milanfar, Generic Action Recognition from a Single Example, Submitted to International Journal of Computer Vision (IJCV), March 29 H. Seo and P. Milanfar, Static and Space-time Visual Saliency Detection by Self- Resemblance, Submitted to Journal of Vision (JoV), May 29 H. Seo and P. Milanfar, Detection of Human Actions from a Single Example, Accepted for publication in International Conference on Computer Vision (ICCV), March 29 H. Seo and P. Milanfar, Nonparametric Bottom-Up Saliency Detection by Self- Resemblance, Accepted for IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1st International Workshop on Visual Scene Understanding (ViSU 9), Miami, June, 29 H. Seo and P. Milanfar, Using Local Regression Kernels for Statistical Object Detection, Proceedings of IEEE International Conference on Image Processing (ICIP), San Diego, 28

48 Conclusions & Future Work Local Steering Kernels are Very Effective Descriptors Simple Approach: PCA + Matrix Cosine Similarity Excellent Detection and Recognition is Achieved without Training Make algorithm scalable for image and (video) retrieval Increase accuracy by incorporating context Detect /recognize objects of interest in general degraded data without explicit restoration

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