Semantic Image Retrieval by Combining Color, Texture and Shape Features

The volume of digital images generated and uploaded on the internet every day by the scientific, medical, educational, industrial and other communities are very large. The problem of retrieving the desired images from huge collections is a major problem. The user queries are becoming very specific and traditional text-based methods cannot efficiently handle them. The subjectivity of human perception and the rich contents of the images further aggravate the problem. To overcome this problem, a new query-by-example technique using multiple color, texture and shape features is proposed and evaluated in this paper. The experimental results suggest that our proposed technique is efficient and retrieves semantically more similar images.

[1]  Tsuhan Chen,et al.  An active learning framework for content-based information retrieval , 2002, IEEE Trans. Multim..

[2]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Thomas S. Huang,et al.  Relevance feedback techniques in interactive content-based image retrieval , 1997, Electronic Imaging.

[4]  Wang Xiaoling,et al.  Enhancing Color histogram for Image Retrieval , 2009 .

[5]  Ramin Zabih,et al.  Histogram refinement for content-based image retrieval , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[6]  Serge J. Belongie,et al.  Region-based image querying , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

[7]  Mircea Nicolescu,et al.  Accurate and Efficient Computation of High Order Zernike Moments , 2005, ISVC.

[8]  Tat-Seng Chua,et al.  An integrated color-spatial approach to content-based image retrieval , 1995, MULTIMEDIA '95.

[9]  Ashish Mohan Yadav,et al.  A Survey on Content Based Image Retrieval Systems , 2014 .

[10]  Miroslaw Pawlak,et al.  On Image Analysis by Moments , 1996, IEEE Trans. Pattern Anal. Mach. Intell..