Image Retrieval based on the Combination of Color Histogram and Color Moment

A novel technique for Content based image retrieval (CBIR) that employs color histogram and color moment of images is proposed. The color histogram has the advantages of rotation and translation invariance and it has the disadvantages of lack of spatial information. In this paper, to improve the retrieval accuracy, a content-based image retrieval method is proposed in which color histogram and color moment feature vectors are combined. For color moment, to improve the discriminating power of color indexing techniques, a minimal amount of spatial information is encoded in the color index by dividing the image horizontally into three equal nonoverlapping regions. The three moments (mean, variance and skewness) are extracted from each region (in this case three regions), for all the color channels. Thus, for a HSV color space, 27 floating point numbers are used for indexing. The HSV (16, 4, 4) quantization scheme has been adopted for color histogram and an image is represented by a vector of 256-dimension. Weights are assigned to each feature respectively and calculate the similarity with combined features of color histogram and color moment using Histogram intersection distance and Euclidean distance as similarity measures. Experimental results show that the proposed method has higher retrieval accuracy in terms of precision than other conventional methods combining color histogram and color moments based on global features approach

[1]  James Lee Hafner,et al.  Efficient Color Histogram Indexing for Quadratic Form Distance Functions , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Juyang Weng,et al.  Hierarchical Discriminant Analysis for Image Retrieval , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Wei-Ying Ma,et al.  Image and Video Retrieval , 2003, Lecture Notes in Computer Science.

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

[5]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  John R. Smith,et al.  Color for Image Retrieval , 2002 .

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

[8]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[9]  Priyanka P. Buch,et al.  Comparative analysis of content based image retrieval using both color and texture , 2011, 2011 Nirma University International Conference on Engineering.

[10]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Bai Xue,et al.  Research of Image Retrieval Based on Color , 2009, 2009 International Forum on Computer Science-Technology and Applications.

[12]  John P. Oakley,et al.  Storage and Retrieval for Image and Video Databases , 1993 .

[13]  Mohan S. Kankanhalli,et al.  Color matching for image retrieval , 1995, Pattern Recognit. Lett..

[14]  Meng Qing-xia.  Research of Image Retrieval Method Based on Color Feature , 2012 .

[15]  Wing W. Y. Ng,et al.  Content-based image retrieval using color moment and Gabor texture feature , 2010, 2010 International Conference on Machine Learning and Cybernetics.

[16]  T. Sudha Rani,et al.  A Novel Image Retrieval Method Using Segmentation and Color Moments , 2012 .

[17]  Michael Stonebraker,et al.  Chabot: Retrieval from a Relational Database of Images , 1995, Computer.

[18]  Ryszard S. Choras,et al.  Integrated color, texture and shape information for content-based image retrieval , 2007, Pattern Analysis and Applications.

[19]  Jau-Ling Shih,et al.  Color Image Retrieval Based on Primitives of Color Moments , 2002, VISUAL.

[20]  Rajshree S. Dubey,et al.  Multi Feature Content Based Image Retrieval , 2010 .

[21]  Vittorio Castelli,et al.  Image Databases: Search and Retrieval of Digital Imagery , 2002 .

[22]  D. Kishore Kumar,et al.  CONTENT BASED IMAGE RETRIEVAL - EXTRACTION BY OBJECTS OF USER INTEREST , 2011 .

[23]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[24]  L.-H. Chen,et al.  Colour image retrieval based on primitives of colour moments , 2002 .

[25]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Markus A. Stricker,et al.  Similarity of color images , 1995, Electronic Imaging.

[27]  Stefan M. Rüger,et al.  Evaluation of Texture Features for Content-Based Image Retrieval , 2004, CIVR.

[28]  Peter Stanchev,et al.  Content-Based Image Retrieval Systems , 2001 .

[29]  Sanjay Silakari,et al.  Image Clustering Using Color and Texture , 2009, 2009 First International Conference on Computational Intelligence, Communication Systems and Networks.

[30]  Wu Xiao-qin.  Research on image retrieval based on color feature , 2008 .