Image Retrieval and Ranking via Consistently Reconstructing Multi-attribute Queries
详细信息 查看全文
- 作者：Xiaochun Cao (19) (20)
Hua Zhang (19)
Xiaojie Guo (20)
Si Liu (21)
Xiaowu Chen (22) - 关键词：Multi ; Attribute Image ; Image Retrieval & Ranking ; Group Sparsity
- 刊名：Lecture Notes in Computer Science
- 出版年：2014
- 出版时间：2014
- 年：2014
- 卷：8689
- 期：1
- 页码：569-583
- 全文大小：3,981 KB
- 参考文献：1. Berg, T.L., Berg, A.C., Shih, J.: Automatic attribute discovery and characterization from noisy web data. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol.?6311, pp. 663-76. Springer, Heidelberg (2010) CrossRef
2. Branson, S., Wah, C., Schroff, F., Babenko, B., Welinder, P., Perona, P., Belongie, S.: Visual recognition with humans in the loop. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol.?6314, pp. 438-51. Springer, Heidelberg (2010) CrossRef
3. Chapelle, O., Le, Q., Smola, A.: Large margin optimization of ranking measures. In: NIPS Workshop on Learning to Rank (2007)
4. Chen, M., Zheng, A., Weinberger, K.: Fast image tagging. In: ICML (2013)
5. Duan, K., Parikh, D., Crandall, D., Grauman, K.: Discovering localized attributes for fine-grained recognition. In: CVPR (2012)
6. Jiang, Z., Lin, Z., Davis, L.: Label consistent k-svd: Learning a discriminative dictionary for recognition. TPAMI?35(11), 2651-664 (2013) CrossRef
7. Joachims, T.: Optimizing search engines using clickthrough data. In: KDD (2002)
8. Kovashka, A., Parikh, D., Grauman, K.: Whittlesearch: Image search with relative attribute feedback. In: CVPR (2012)
9. Kumar, N., Belhumeur, P., Nayar, S.: FaceTracer: A search engine for large collections of images with faces. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol.?5305, pp. 340-53. Springer, Heidelberg (2008) CrossRef
10. Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and simile classifiers for face verification. In: ICCV (2009)
11. Lin, Z., Ding, G., Hu, M., Wang, J., Ye, X.: Image tag completion via image-specific and tag-specific linear sparse reconstructions. In: CVPR (2013)
12. Liu, J., Ji, S., Ye, J.: Multi-task feature learning via efficient l2,1-norm minimization. In: UAI (2009)
13. Liu, S., Song, Z., Liu, G., Xu, C., Lu, H., Yan, S.: Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set. In: CVPR (2012)
14. Petterson, J., Caetano, T.: Reverse multi-label learning. In: NIPS (2010)
15. Rastegari, M., Diba, A., Parikh, D.: Multi-attribute queries: To merge or not to merge? In: CVPR (2013)
16. Scheirer, W., Kumar, N., Belhumeur, P.N., Boult, T.E.: Multi-attribute spaces: Calibration for attribute fusion and similarity search. In: CVPR (2012)
17. Siddiquie, B., Feris, R.S., Davis, L.S.: Image ranking and retrieval based on multi-attribute queries. In: CVPR (2011)
18. Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large margin methods for structured and interdependent output variables. JMLR?6, 1453-484 (2005)
19. Wang, C., Yan, S., Zhang, L., Zhang, H.J.: Multi-label sparse coding for automatic image annotation. In: CVPR (2009)
20. Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., Perona, P.: The caltech-ucsd birds-200-2011 dataset. California Institute of Technology, CNS-TR-2011-001 (2007)
21. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. TPAMI?31(2), 210-27 (2009) CrossRef
22. Wu, L., Jin, R., Jain, A.K.: Tag completion for image retrieval. TPAMI?35(3), 716-27 (2013) CrossRef
23. Yu, F., Ji, R., Tsai, M.H., Ye, G., Chang, S.F.: Weak attributes for large-scale image retrieval. In: CVPR (2012)
24. Zhang, M., Zhou, Z.: Ml-knn: A lazy learning approach to multi-label learning. PR?40(7), 2038-048 (2007)
25. Zhang, S., Huang, J., Li, H., Metaxas, D.N.: Automatic image annotation and retrieval using group sparsity. TSMC, Part B, 838-49 (2012)
26. Zheng, M., Bu, J., Chen, C., Wang, C., Zhang, L., Qiu, G., Cai, D.: Graph regularized sparse coding for image representation. TIP?20(5), 1327-336 (2011) - 作者单位：Xiaochun Cao (19) (20)
Hua Zhang (19)
Xiaojie Guo (20)
Si Liu (21)
Xiaowu Chen (22)
19. School of Computer Science and Technology, Tianjin University, Tianjin, China
20. State Key Laboratory of Information Security, IIE, Chinese Academy of Sciences, China
21. Department of Electrical & Computer Engineering, National University of Singapore, Singapore
22. State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China - ISSN：1611-3349
文摘
Image retrieval and ranking based on the multi-attribute queries is beneficial to various real world applications. Traditional methods on this problem often utilize intermediate representations generated by attribute classifiers to describe the images, and then the images in the database are sorted according to their similarities to the query. However, such a scheme has two main challenges: 1) how to exploit the correlation between query attributes and non-query attributes, and 2) how to handle noisy representations since the pre-defined attribute classifiers are probably unreliable. To overcome these challenges, we discover the correlation among attributes via expanding the query representation, and imposing the group sparsity on representations to reduce the disturbance of noisy data. Specifically, given a multi-attribute query matrix with each row corresponding to a query attribute and each column the pre-defined attribute, we firstly expand the query based on the correlation of the attributes learned from the training data. Then, the expanded query matrix is reconstructed by the images in the dataset with the ?sub class="a-plus-plus">2,1 regularization. Furthermore, we introduce the ranking SVM into the objective function to guarantee the ranking consistency. Finally, we adopt a graph regularization to preserve the local visual similarity among images. Extensive experiments on LFW, CUB-200-2011, and Shoes datasets are conducted to demonstrate the effectiveness of our proposed method.