A Flexible Hierarchical Classification Algorithm for Content Based Image Retrieval

The goal of paper is to describe a flexible hierarchical classification algorithm and a new image similarity computing model based on mixing several image features for promoting the performance and efficiency of speed for content-based image retrieval. With an experimental comparison of a large number of different representative point selection approach, we are trying to seek for a method of uniform division of image space, eventually design a novel approach enlightening by high-dimensional indexing and social networking, that introduces the directivity to image classification that is used to explain the convergence of images to edge points of the high-dimension feature space in this paper. Meanwhile we find the laws of parameter setting of this algorithm through experiments and these laws acquires satisfied effects in different dataset. In addition to that algorithm, we also find some features assembling with reasonable formula to represent images better in color, texture and shape. Experimental results based on a database of about 50,000 person images demonstrate improved performance, as compare with other combinations in our descriptor set consisting of several general features mentioned below.

[1]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[2]  Ilya Markov.  VP-tree: Content-Based Image Indexing , 2007, SYRCoDIS.

[3]  Miroslaw Bober,et al.  MPEG-7 visual shape descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..

[4]  Fernando C. Monteiro.  A Survey : Image Segmentation Techniques 89 International Journal of Future Computer and Communication , 2013 .

[5]  Kwang-Kyu Seo,et al.  An application of one-class support vector machines in content-based image retrieval , 2007, Expert Syst. Appl..

[6]  Hermann Ney,et al.  Features for image retrieval: an experimental comparison , 2008, Information Retrieval.

[7]  C. Won,et al.  Efficient Use of MPEG‐7 Edge Histogram Descriptor , 2002 .

[8]  Virgílio A. F. Almeida,et al.  From bias to opinion: a transfer-learning approach to real-time sentiment analysis , 2011, KDD.

[9]  Yiannis S. Boutalis,et al.  CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval , 2008, ICVS.

[10]  Nesar Ahmad,et al.  Unsupervised Content Based Image Retrieval by Combining Visual Features of an Image With A Threshold , 2012 .

[11]  Anil K. Jain,et al.  Image classification for content-based indexing , 2001, IEEE Trans. Image Process..

[12]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.

[13]  Shin'ichi Satoh,et al.  The SR-tree: an index structure for high-dimensional nearest neighbor queries , 1997, SIGMOD '97.

[14]  Mohamed Abdel-Mottaleb,et al.  Hierarchical clustering algorithm for fast image retrieval , 1998, Electronic Imaging.