Content-Based Image Retrieval Using Integrated Color, Texture, and Shape Features

In this chapter, a content-based image retrieval technique based on the concept of region-based image retrieval has been described. This technique integrates color, texture, and shape features using local binary patterns (LBPs). In this technique, the image is divided into a fixed number of blocks and from each block LBP-based color, texture, and shape features are computed. The color and texture features are extracted using LBP histograms of quantized color image and gray-level images, respectively. Shape features are computed using the binary edge map obtained using Sobel edge detector from each block. All three features are combined to make a single completed binary region descriptor (CBRD) represented in the LBP way. To support region-based retrieval, an effective region code-based scheme is employed. In this technique, the spatial relative locations of objects are also considered to increase the retrieval accuracy.

[1]  Fernando Pereira,et al.  MPEG-7 the generic multimedia content description standard, part 1 - Multimedia, IEEE , 2001 .

[2]  Chaobing Huang,et al.  Regions of interest extraction from color image based on visual saliency , 2011, The Journal of Supercomputing.

[3]  Louis Vuurpijl,et al.  The utilization of human color categorization for content-based image retrieval , 2004, IS&T/SPIE Electronic Imaging.

[4]  Vipin Tyagi,et al.  Region Based Image Retrieval Using Integrated Color, Texture and Shape Features , 2015 .

[5]  Jingyu Yang,et al.  Image retrieval based on the texton co-occurrence matrix , 2008, Pattern Recognit..

[6]  Xingyuan Wang,et al.  A novel method for image retrieval based on structure elements' descriptor , 2013, J. Vis. Commun. Image Represent..

[7]  Seok-Wun Ha,et al.  ROI Based Natural Image Retrieval Using Color and Texture Feature , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[8]  Henning Biermann,et al.  Regions-of-Interest and Spatial Layout for Content-Based Image Retrieval , 2001, Multimedia Tools and Applications.

[9]  J. Nang,et al.  Content-Based Image Retrieval Method using the Relative Location of Multiple ROIs , 2011 .

[10]  Lai-Man Po,et al.  MIRROR: an interactive content based image retrieval system , 2005, 2005 IEEE International Symposium on Circuits and Systems.

[11]  Rung Ching Chen,et al.  A ROI image retrieval method based on CVAAO , 2008, Image Vis. Comput..

[12]  Vipin Tyagi,et al.  An integrated approach for image retrieval using local binary pattern , 2015, Multimedia Tools and Applications.

[13]  Lei Zhang,et al.  Image retrieval based on micro-structure descriptor , 2011, Pattern Recognit..

[14]  Alex Pentland,et al.  Photobook: Content-based manipulation of image databases , 1996, International Journal of Computer Vision.

[15]  Kanad K. Biswas,et al.  Region-based image retrieval using integrated color, shape, and location index , 2004, Comput. Vis. Image Underst..

[16]  Vipin Tyagi,et al.  Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching , 2014, Inf. Sci..

[17]  Christos Faloutsos,et al.  Efficient and effective Querying by Image Content , 1994, Journal of Intelligent Information Systems.

[18]  B. S. Manjunath,et al.  NeTra: A toolbox for navigating large image databases , 1997, Multimedia Systems.

[19]  Qi Tian,et al.  Combine user defined region-of-interest and spatial layout for image retrieval , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[20]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[21]  Amarnath Gupta,et al.  Visual information retrieval , 1997, CACM.

[22]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[23]  Vipin Tyagi,et al.  An effective scheme for image texture classification based on binary local structure pattern , 2013, The Visual Computer.