Large Scale Image Retrieval by Coupled Binary Embedding

International Journal of Industrial Engineering

© 2014 by SSRG - IJIE Journal

Volume 1 Issue 2

Year of Publication : 2014

Authors : S.Raguvel and Miss D.Dharini

10.14445/23499362/IJIE-V1I2P101

How to Cite?

S.Raguvel and Miss D.Dharini, "Large Scale Image Retrieval by Coupled Binary Embedding," SSRG International Journal of Industrial Engineering, vol. 1,  no. 2, pp. 1-5, 2014. Crossref, https://doi.org/10.14445/23499362/IJIE-V1I2P101

Abstract:

Visual matching is a crucial step in image retrieval based on the bag-of-words (BoW) model. In the baseline method, two key points are reconsidered as a matching pair if their SIFT descriptors are quantized to the same visual word. However, t he SIFT visual word has two limitations. First, it loses most of its discriminative power during quantization. Second, SIFT only describes the local texture feature. Both drawbacks impair the discriminative power of the BoW model and lead to false positive matches. To tackle this problem, this paper proposes to embed multiple binary features at indexing level. To model correlation between features, a multi-IDF scheme is introduced, through which different binary features are coupled into the inverted file. We show that matching verification methods based on binary features, such as Hamming embedding, can be effectively incorporated in our framework. As, we explore the fusion of binary color feature into image retrieval. The joint integration of the SIFT visual word and binary features greatly enhances the precision of visual matching, reducing the impact of false positive matches. Our method is evaluated through extensive experiments on four benchmark datasets (Ukbench, Holidays, Dup Image and MIR Flickr 1M). We show that our method significantly improves the baseline approach. In addition, large-scale experiments indicate that the proposed method requires acceptable memory usage and query time compared with other approaches. Further, when global color feature is integrated, our method yields competitive performance with the state-of-the-arts

Keywords:

—Feature fusion, coupled binary embedding, multi IDF, image retrieval.

References:

[1] H.Jégou, M. Douze, and C.Schmid” Hamming embedding and weak geometric consistency for large scale image search,” in Proc. 10th Eur.Conf. Comput. Vis. ECCV, 2008, pp. 304–317.
[2] W. Lu, J. Wang, X.-S. Hua, S.Wangand S. Li “Contextual image search,” in Proc. 19th ACM Multimedia, 2011, pp. 513–522.
[3] X. Li, Y.-J.Zhang, B.Shen and B.-D. Liu, “Image tag completion by low-rank factorization with dual reconstruction structure preserved,” in Proc. IEEE Int. Conf, Image Process. (ICIP), Oct. 2014.
[4] J. Sivic and A. Zisserman, “Video Google: A text retrieval approach to object matching in videos,” in Proc. IEEE Int. Conf. Comput. Vis.,(ICCV), Oct. 2003, pp. 1470–1477.
[5] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,”Int. J. Comput. Vis.,vol. 60, no. 2, pp. 91–110, 2004.
[6] J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, “Object retrieval with large vocabularies and fast spatial matching,” in Proc.Comput. Vis. Pattern Recognit. (CVPR), 2007, pp. 1–8.
[7] D. Nister and H. Stewenius, “Scalable recognition with a vocabulary tree,” in Proc. Comput. Vis. Pattern Recognit. (CVPR),vol.2. 2006, pp. 2161–2168.
[8] L.Zheng, S.Wang, Z. Liu, and Q. Tian, “Lp-norm IDF for large scale image search,”in Proc. IEEE Comput. Vis. Pattern Recognit. (CVPR),Jun.2013, pp. 1626–633.
[9] L. Zheng,S. Wang, and Q. Tian, “Lp norm IDF for scalable image retrieval,”IEEE Trans. Image Process., to be published.
[10] D. Qin and C. W. L. van Gool, “Query adaptive similarity for large scale object retrieval,” in Proc. IEEE Comput. Vis. Pattern Recognit. (CVPR),Jun. 2013, pp. 1610–1617.
[11] G. Tolias, Y. Avrithis, and H. Jégou, “To aggregate or not to aggregate: Selective match kernels for image search,” in Proc. IEEE Int. Conf.Comput. Vis., (ICCV), Dec. 2013, pp. 1401–1408.
[12] Z. Liu, H. Li, W. Zhou, R. Zhao, and Q. Tian, “Contextual hashing forlarge-scale image search,” IEEE Trans. Image Process., vol. 23, no. 4,pp. 1606–1614, Apr. 2014.
[13] C.Wengert, M. Douze, and H. Jégou, “Bag-of-colors for improved image search,” in Proc. 19th ACM Multimedia, 2011, pp. 1437–1440.
[14] R. Arandjelovic and A. Zisserman, “Three things everyone should know to improve object retrieval,” in Proc. IEEE Comput. Vis. Pattern Recognit. (CVPR), Jun. 2012, pp. 2911–2918.
[15] K.Simonyan, A. Vedaldi, and A. Zisserman, “Descriptor learning using convex optimisation,” in Proc. 12th Eur. Conf. Comput. Vis. (ECCV),Oct. 2012, pp. 243–256.
[16] H. Jégou, M. Douze, and C. Schmid, “Improving bag-of-features for large scale image search,” Int. J. Comput.Vis., vol. 87, no. 3, pp. 316–336, 2010.
[17] J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, “Lost in quantization: Improving particular object retrieval in large scale image databases,” in Proc. IEEE Comput. Vis. Pattern Recognit. (CVPR), Jun. 2008, pp. 1–8.
[18] F. Perronnin, J. Sánchez, and T. Mensink, “Improving the fisher kernel for large-scale image classification,” in Proc. 11th Eur. Conf. Comput.Vis. (ECCV), 2010, pp. 143–156.
[19] L.Xie, Q. Tian, and B. Zhang, “Spatial pooling of heterogeneous features for image classification,” IEEE Trans. Image Process., vol. 23, no. 5, pp. 1994–2008, May 2014.
[20] W. Zhou, H. Li, Y. Lu, and Q. Tian, “Principal visual word discovery for automatic license plate detection,” IEEE Trans. Image Process., vol. 21, no. 9, pp. 4269–4279, Sep. 2012.