Retrieval of User Specific Images Using Semantic Signatures

Authors: K. Venkateswari, U. K. Balaji Saravanan, K. Thangaraj, K. V. Deepana

Abstract:

Image search engines rely on the surrounding textual keywords for the retrieval of images. It is a tedious work for the search engines like Google and Bing to interpret the user’s search intention and to provide the desired results. The recent researches also state that the Google image search engines do not work well on all the images. Consequently, this leads to the emergence of efficient image retrieval technique, which interprets the user’s search intention and shows the desired results. In order to accomplish this task, an efficient image re-ranking framework is required. Sequentially, to provide best image retrieval, the new image re-ranking framework is experimented in this paper. The implemented new image re-ranking framework provides best image retrieval from the image dataset by making use of re-ranking of retrieved images that is based on the user’s desired images. This is experimented in two sections. One is offline section and other is online section. In offline section, the reranking framework studies differently (reference classes or Semantic Spaces) for diverse user query keywords. The semantic signatures get generated by combining the textual and visual features of the images. In the online section, images are re-ranked by comparing the semantic signatures that are obtained from the reference classes with the user specified image query keywords. This re-ranking methodology will increases the retrieval image efficiency and the result will be effective to the user.

Keywords: CBIR, Image Re-ranking, Image Retrieval, Semantic Signature, Semantic Space.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099272

References:

[1] Xiaogang Wang, Shi Qiu, Ke Liu, and Xiaoou Tang, Web Image Re- Ranking Using Query-Specific Semantic Signatures IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 36, No. 4, April 2014
[2] Bin Wang, Zhiwei Li, Mingjing Li, Wei-Ying Ma: Large-Scale Duplicate Detection For Web Image Search, ©2006 IEEE
[3] Lukasz Kobyli´nski and Krzysztof Walczak: Color Mining of Images Based on Clustering, Proceedings of the International Multiconference on Computer Science and Information Technology ISSN 1896-7094 2007
[4] Dr. Sanjay Silakari , Dr. Mahesh Motwani , and Manish Maheshwari : Color Image Clustering using Block Truncation Algorithm, IJCSI International Journal of Computer Science Issues, Vol. 4, No. 2, 2009
[5] A.Kannan,Dr.V.Mohan,Dr.N.Anbazhagan:Image Clustering and Retrieval using Image Mining Techniques,2010 IEEE International Conference on Computational Intelligence and Computing Research.
[6] Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio : Large Scale Online Learning of Image Similarity Through Ranking, Journal of Machine Learning Research 11 (2010) 1109-1135
[7] B.Ramamurthy, K.R.Chandran CBMIR: Shape-Based Image Retrieval Using Canny Edge Detection And K-Means Clustering Algorithms For Medical Images, International Journal of Engineering Science and Technology (IJEST) ISSN : 0975-5462 Vol. 3 No. 3 March 2011 1870
[8] Xiaoou Tang, Ke Liu, Jingyu Cui, Fang Wen, IntentSearch: Capturing User Intention For One-Click Internet Image Search IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 34, No. 7, July 2012
[9] XiaoouTang, Wei Luo, and Xiaogang Wang,Content-Based Photo Quality Assessment, IEEE Transactions on Multimedia, Copyright (c) 2013 IEEE
[10] Kalyan Roy, Joydeep Mukherjee: Image Similarity Measure using Color Histogram, Color Coherence Vector, and Sobel Method,International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064- JAN 2013
[11] Yin-Hsi Kuo , Wen-Huang Cheng, Hsuan-Tien Lin, Winston H. Hsu Unsupervised Semantic Feature Discovery for Image Object Retrieval and Tag Refinement, IEEE Transactions on Multimedia, Vol. 14, no. 4, August 2012
[12] N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2005.
[13] M. Unser, , “Texture Classification and Segmentation Using Wavelet Frames,” IEEE Trans. Image Processing , vol. 4, no. 11, pp. 1549- 1560, Nov. 1995.
[14] A. Torralba, K. Murphy, W. Freeman, and M. Rubin, “Context-Based Vision System for Place and Object Recognition,” Proc.Ninth IEEE Int’l Conf. Computer Vision (ICCV), 2003.