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Automatic Image Annotation Using Group Sparsity

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. Introductions.

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Automatic Image Annotation Using Group Sparsity

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  1. Automatic Image Annotation Using Group Sparsity Shaoting Zhang1, Junzhou Huang1, Yuchi Huang1, Yang Yu1, Hongsheng Li2, Dimitris Metaxas1 1CBIM, Rutgers University, NJ 2IDEA 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 • L2 norm regularization • L1 norm regularization • Group sparsity based regularization • Obtain Image Pairs • Experiments

  5. Regularization based Feature Selection • Given similar/dissimilar image pair list (P1,P2) …… …… …… …… …… …… …… …… …… …… …… …… …… …… …… …… …… …… …… …… …… FP1 FP2 X

  6. Regularization based Feature Selection 1 -1 1 1 … … … … … X w Y

  7. Regularization based Feature Selection • Annotation framework Weights Similarity Testing input High similarity Training data

  8. Regularization based Feature Selection • L2 regularization • Robust, solvable: (XTX+λI)-1XTY • No sparsity % w Histogram of weights

  9. Regularization based Feature Selection • L1 regularization • Convex optimization • Basis pursuit, Grafting, Shooting, etc. • Sparsity prior % w Histogram of weights

  10. Regularization based Feature Selection RGB HSV • Group sparsity[1] • L2 inside the same group, L1 for different groups • Benefits: removal of whole feature groups • Projected-gradient[2] =0 ≠0 [1] M. Yuan and Y. Lin. Model selection and estimation in regressionwith grouped variables. Journal of the Royal Statistical Society,Series B, 68:49–67, 2006. [2] E. Berg, M. Schmidt, M. Friedlander, and K. Murphy. Group sparsityvia linear-time projection. In Technical report, TR-2008-09, 2008. http://www.cs.ubc.ca/~murphyk/Software/L1CRF/index.html

  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. Distance histogram of similar pairs Distance histogram of all pairs [1] A. Makadia, V. Pavlovic, and S. Kumar. A new baseline for image annotation. In ECCV, pages 316–329, 2008.

  13. Obtain Image Pairs • Inspired by the relevance feedback and the expectation maximization method. k1 nearest k2 farthest (candidates of dissimilar pairs) (candidates of similar 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. 2006.

  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, 2007. [2] A. Makadia, V. Pavlovic, and S. Kumar. A new baseline for image annotation. In ECCV, pages 316–329, 2008. [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 Precision Recall N+ Corel5K IIAPR TC12

  18. Evaluation of Generality • Weights computed from Corel5K, then applied on IAPR TC12. N+ Precision Recall λ λ λ

  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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