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Automatic Image Annotation Using Group Sparsity Shaoting Zhang 1, Junzhou Huang 1, Yuchi Huang 1, Yang Yu 1, Hongsheng Li 2, Dimitris Metaxas 1 1 CBIM,

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Presentation on theme: "Automatic Image Annotation Using Group Sparsity Shaoting Zhang 1, Junzhou Huang 1, Yuchi Huang 1, Yang Yu 1, Hongsheng Li 2, Dimitris Metaxas 1 1 CBIM,"— Presentation transcript:

1 Automatic Image Annotation Using Group Sparsity Shaoting Zhang 1, Junzhou Huang 1, Yuchi Huang 1, Yang Yu 1, Hongsheng Li 2, Dimitris Metaxas 1 1 CBIM, Rutgers University, NJ 2 IDEA Lab, Lehigh University, PA

2 Introductions Goal: image annotation is to automatically assign relevant text keywords to any given image, reflecting its content. Previous methods: – Topic models [Barnard, et.al., J. Mach. Learn Res.’03; Putthividhya, et.al., CVPR’10] – Mixture models [Carneiro, et.al., TPAMI’07; Feng, et.al., CVPR’04] – Discriminative models [Grangier, et.al., TPAMI’08; Hertz, et.al., CVPR’04] – Nearest neighbor based methods [Makadia, et.al., ECCV’08; Guillaumin, et.al., ICCV’09]

3 Introductions Limitations: – Features are often preselected, yet the properties of different features and feature combinations are not well investigated in the image annotation task. – Feature selection is not well investigated in this application. Our method and contributions: – Use feature selection to solve annotation problem. – Use clustering prior and sparsity prior to guide the selection.

4 Outline Regularization based Feature Selection – Annotation framework – L 2 norm regularization – L 1 norm regularization – Group sparsity based regularization Obtain Image Pairs Experiments

5 Regularization based Feature Selection Given similar/dissimilar image pair list (P1,P2) …… XF P1 F P2

6 Regularization based Feature Selection X 1 1 … wY

7 Regularization based Feature Selection Annotation framework Testing input Training data WeightsSimilarity High similarity

8 Regularization based Feature Selection L 2 regularization Robust, solvable: (X T X+λI) -1 X T Y No sparsity w % Histogram of weights

9 Regularization based Feature Selection L 1 regularization Convex optimization Basis pursuit, Grafting, Shooting, etc. Sparsity prior Histogram of weights w %

10 Regularization based Feature Selection Group sparsity [1] L 2 inside the same group, L 1 for different groups Benefits: removal of whole feature groups Projected-gradient [2] [1] M. Yuan and Y. Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, Series B, 68:49–67, [2] E. Berg, M. Schmidt, M. Friedlander, and K. Murphy. Group sparsity via linear-time projection. In Technical report, TR , =0≠0 RGB HSV

11 Outline Regularization based Feature Selection Obtain Image Pairs – Only rely on keyword similarity – Also rely on feedback information Experiments

12 Obtain Image Pairs Previous method [1] solely relies on keyword similarity, which induces a lot of noise. [1] A. Makadia, V. Pavlovic, and S. Kumar. A new baseline for image annotation. In ECCV, pages 316–329, Distance histogram of similar pairsDistance histogram of all pairs

13 Obtain Image Pairs Inspired by the relevance feedback and the expectation maximization method. k1 nearest k2 farthest (candidates of similar pairs) (candidates of dissimilar pairs)

14 Outline Regularization based Feature Selection Obtain Image Pairs Experiments – Experimental settings – Evaluation of regularization methods – Evaluation of generality – Some annotation results

15 Experimental Settings Data protocols – Corel5K (5k images) – IAPR TC12 [1] (20k images) Evaluation – Average precision – Average recall – #keywords recalled (N+) [1] M. Grubinger, P. D. Clough, H. Muller, and T. Deselaers. The iapr tc-12 benchmark - a new evaluation resource for visual information systems

16 Experimental Settings Features – RGB, HSV, LAB – Opponent – rghistogram – Transformed color distribution – Color from Saliency [1] – Haar, Gabor [2] – SIFT [3], HOG [4] [1] X. Hou and L. Zhang. Saliency detection: A spectral residual approach. In CVPR, [2] A. Makadia, V. Pavlovic, and S. Kumar. A new baseline for image annotation. In ECCV, pages 316–329, [3] K. van de Sande, T. Gevers, and C. Snoek. Evaluating color descriptors for object and scene recognition. PAMI, 99(1),2010. [4] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, pages 886–893, 2005.

17 Evaluation of Regularization Methods Corel5K IIAPR TC12 PrecisionRecallN+

18 Evaluation of Generality PrecisionRecall N+ Weights computed from Corel5K, then applied on IAPR TC12. λ λλ

19 Some Annotation Results

20 Conclusions and Future Work Conclusions – Proposed a feature selection framework using both sparsity and clustering priors to annotate images. – The sparse solution improves the scalability. – Image pairs from relevance feedback perform much better. Future work – Different grouping methods. – Automatically find groups (dynamic group sparsity). – More priors (combine with other methods). – Extend this framework to object recognition.

21 Thanks for listening


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