A New Mobile Visual Search System Based on the Human Visual System

Abstract:

We describe a novel mobile visual search system based on the saliencymechanism and sparse coding principle of the human visual system (HVS). In the featureextraction step, we first divide an image into different regions using thesaliency extraction algorithm. Then scale-invariant feature transform (SIFT)descriptors in all regions are extracted while regional identities arepreserved based on their various saliency levels. According to the sparsecoding principle in the HVS, we adopt a local neighbor preserving Hash functionto establish the binary sparse expression of the SIFT features. In the searchingstep, the nearest neighbors matched to the hashing codes are processed accordingto different saliency levels. Matching scores of images in the database arederived from the matching of hashing codes. Subsequently, the matching scoresof all levels are weighed by degrees of saliency to obtain the initial set of results. In order to further ensure matching accuracy, we propose an optimized retrieval scheme based on global texture information. We conduct extensive experiments on an actual mobile platform in large-scale datasets by using Corel-1000. The resultsshow that the proposed method outperforms the state-of-the-art algorithms on accuracyrate, and no significant increase in the running time of the feature extractionand retrieval can be observed.

• Periodical: Applied Mechanics and Materials (Volume 461)
• Pages: 792-800
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.461.792
• Online since: November 2013
• Authors: Bo Zhao, Hong Wei Zhao, Ping Ping Liu, Gui He Qin
• Keywords: Hash, Human Vision System, Mobile Vision Search, Saliency, SIFT Descriptor, Sparse Coding

References

[1] Google, GoogleGoggles, in, (2011).

[2] Amazon, Snaptell, in, (2007).

[3] Nokia, Point and Find, in, (2006).

[4] M. Calonder, V. Lepetit, C. Strecha, P. Fua, Brief: Binary robust independent elementary features, Computer Vision–ECCV 2010 (2010), pp.778-792.

DOI: 10.1007/978-3-642-15561-1_56

[5] V. Chandrasekhar, G. Takacs, D. Chen, S. Tsai, R. Grzeszczuk, B. Girod, CHoG: Compressed histogram of gradients A low bit-rate feature descriptor, in: Computer Vision and Pattern Recognition, 2009, pp.2504-2511.

DOI: 10.1109/cvpr.2009.5206733

[6] Lowe, David G. (1999). Object recognition from local scale-invariant features. Proceedings of the International Conference on Computer Vision. 1999, p.1150–1157.

DOI: 10.1109/iccv.1999.790410

[7] V. Chandrasekhar, G. Takacs, D. Chen, S.S. Tsai, J. Singh, B. Girod, Transform coding of image feature descriptors, in: IS&T/SPIE Electronic Imaging, International Society for Optics and Photonics, 2009, pp.725710-725719.

DOI: 10.1117/12.805982

[8] D.M. Chen, S.S. Tsai, V. Chandrasekhar, G. Takacs, J. Singh, B. Girod, Tree histogram coding for mobile image matching, in: Data Compression Conference, 2009., pp.143-152.

DOI: 10.1109/dcc.2009.33

[9] R. Salakhutdinov, G. Hinton, Semantic hashing, International Journal of Approximate Reasoning, 50 (2009), pp.969-978.

DOI: 10.1016/j.ijar.2008.11.006

[10] A. Gionis, P. Indyk, R. Motwani, Similarity search in high dimensions via hashing, in: Proceedings of the International Conference on Very Large Data Bases, 1999, pp.518-529.

[11] Y. Weiss, A. Torralba, R. Fergus, Spectral hashing, in, NIPS, (2008).

[12] W. Kong, W.J. Li, M. Guo, Manhattan hashing for large-scale image retrieval, in: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, ACM, 2012, pp.45-54.

DOI: 10.1145/2348283.2348293

[13] H. Jae-Pil, L. Youngwoon, H. Junfeng, C. Shih-Fu, Y. Sung-Eui, Spherical hashing, in: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012, pp.2957-2964.

DOI: 10.1109/cvpr.2012.6248024

[14] Mobile Product Search with Bag of Hash Bits and Boundary Reranking. Junfeng He, Jinyuan Feng, Xianglong Liu, Tao Cheng, Tai-Hsu Lin, Hyunjin Chung, Shih-Fu Chang In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Providence, Rhode Island June, (2012).

DOI: 10.1109/cvpr.2012.6248030

[15] Itti L，Koch C，Niebur E. A model of saliency-based visual attention for rapid scene analysis［J］. IEEE Trans on Pattern Analysis and Machine Intelligence. 1998, 20(11) : 1254-1259.

DOI: 10.1109/34.730558

[16] Zhao Hong-wei, Chen Xiao, Liu Ping-ping, Geng Qing-tian. Adaptive segmentation for visual salient object[J]. Optics and Precision Engineering. 2013 Vol. 21 (2): 531-538.

DOI: 10.3788/ope.20132102.0531

[17] Michison G. The organization of sequential memory: sparse representation and the targeting problem [J], In: Seelen W V, Shaw G. 1einbos U M. Organization of Neural Networks. VCH Verlagsgesellsehaft, Weinheim, 1988. 347—367.

[18] Jolliffe I.T. Principal Component Analysis [M]. Springer-Verlag, (1986).

[19] Jae-Pil Heo, Youngwoon Lee, Junfeng He, Shih-Fu Chang, Sung-Eui Yoon. Spherical Hashing. In Proc. CVPR, (2012).

DOI: 10.1109/cvpr.2012.6248024

[20] R M Haralick, K Shangmugam, etc. Texture feature for image classification [J]. IEEE Transaction on systems, 1973, SMC-3 (6): 768-780.

[21] Corel database. Corel Corporation, Corel Gallery 3. 0. Available in James Z. Wang's Research Group: http: /wang. ist. psu. edu/ jwang/test1. tar.