A Review: Color Feature Extraction Methods for Content Based Image Retrieval

For more than a decade Content Based Image Retrieval is topic of interest for researcher. The three primitive visual features, namely, color, texture and shape refers to the term ‘content’ in content based image retrieval. Although visual features cannot be completely determined by semantic features, but still semantic features are used because they are easier to integrate into mathematical formulations. Therefore good visual feature extraction is one of the important task for representing image compactly. Among the visual features, colors is the most vital, reliable and widely used feature. This paper reviews various methods, namely Global Color Histogram, Histogram Intersection, Image Bitmap, Local Color Histogram, Color Correlogram, etc., employed to extract the color feature. This paper briefly elaborates these different methods of color feature extraction and then presents a comparative study for selection of these methods in various applications.

[1]  Ming-Syan Chen,et al.  Adaptive Color Feature Extraction Based on Image Color Distributions , 2010, IEEE Transactions on Image Processing.

[2]  Husniza Husni,et al.  A weighted dominant color descriptor for content-based image retrieval , 2013, J. Vis. Commun. Image Represent..

[4]  Yang Jingyi.  Stone Images Retrieval Based on Color Histogram , 2009 .

[5]  Chuen-Horng Lin,et al.  Image Retrieval System Based on Adaptive Color Histogram and Texture Features , 2011, Comput. J..

[6]  Wei-Han Chang,et al.  A fast MPEG-7 dominant color extraction with new similarity measure for image retrieval , 2008, J. Vis. Commun. Image Represent..

[7]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[8]  Jing Huang,et al.  Image indexing using color correlograms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Sung Bum Pan,et al.  Image Retrieval Using Maximum Frequency of Local Histogram Based Color Correlogram , 2008, 2008 International Conference on Multimedia and Ubiquitous Engineering (mue 2008).

[10]  Hong Shao,et al.  Image Retrieval Based on MPEG-7 Dominant Color Descriptor , 2008, 2008 The 9th International Conference for Young Computer Scientists.

[11]  Sébastien Lefèvre,et al.  Morphological Description of Color Images for Content-Based Image Retrieval , 2009, IEEE Transactions on Image Processing.

[12]  Xiangyang Wang,et al.  A new content-based image retrieval technique using color and texture information , 2013, Comput. Electr. Eng..

[13]  Chin-Chen Chang,et al.  Color image retrieval technique based on color features and image bitmap , 2007, Inf. Process. Manag..

[14]  Jing-Yu Yang,et al.  Content-based image retrieval using color difference histogram , 2013, Pattern Recognit..

[15]  Georgios S. Paschos,et al.  Image Content-Based Retrieval Using Chromaticity Moments , 2003, IEEE Trans. Knowl. Data Eng..

[16]  Garrett M. Johnson,et al.  Derivation of a color space for image color difference measurement , 2010 .

[17]  Rohini K. Srihari,et al.  Spatial color histograms for content-based image retrieval , 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence.

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

[19]  Rong-Tai Chen,et al.  A smart content-based image retrieval system based on color and texture feature , 2009, Image Vis. Comput..

[20]  Kai-Kuang Ma,et al.  Fuzzy color histogram and its use in color image retrieval , 2002, IEEE Trans. Image Process..

[21]  Chien-Hsing Chou,et al.  Short Papers , 2001 .

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

[23]  Kerry Rodden,et al.  Evaluating similarity-based visualisations as interfaces for image browsing , 2002 .

[24]  C. S. Gode,et al.  Enhancement of Image Retrieval by Using Colour, Texture and Shape Features , 2014, 2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies.