Content-Based Image Retrieval Based on Location-Independent Regions of Interest

In this chapter, a technique of object-based image retrieval to retrieve the images based on location-independent region of interest (ROI) is given. In this technique, instead of extracting the features of the whole query image, features of the objects of interest are extracted using morphological operations. First, background subtraction is performed to reduce the effect of background intensities, then segmentation is performed, and the regions are extracted. To minimize the number of comparisons in image retrieval process, the image is categorized into texture and non-texture regions. Tetrolet transform is used to retrieve the texture features for the texture regions, while moment invariants and edge features are used for non-texture regions.

[1]  Masayuki Kimura,et al.  A method for extracting region of interest based on attractiveness , 2006, IEEE Transactions on Consumer Electronics.

[2]  C. Koch,et al.  A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.

[3]  Thomas Sikora,et al.  The MPEG-7 visual standard for content description-an overview , 2001, IEEE Trans. Circuits Syst. Video Technol..

[4]  Kin-Man Lam,et al.  Face-image retrieval based on singular values and potential-field representation , 2014, Signal Process..

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

[6]  T. Kanimozhi,et al.  An integrated approach to region based image retrieval using firefly algorithm and support vector machine , 2015, Neurocomputing.

[7]  Kien A. Hua,et al.  Image Retrieval Based on Regions of Interest , 2003, IEEE Trans. Knowl. Data Eng..

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

[9]  Neill W. Campbell,et al.  Iterative refinement by relevance feedback in content-based digital image retrieval , 1998, MULTIMEDIA '98.

[10]  Pietro Perona,et al.  Graph-Based Visual Saliency , 2006, NIPS.

[11]  James Ze Wang,et al.  Classification of textured and non-textured images using region segmentation , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

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

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

[14]  Vipin Tyagi,et al.  A Survey on Texture Image Retrieval , 2016 .

[15]  Re Gonzalez,et al.  R.C. Eddins, Digital image processing using MATLAB, vol. Gatesmark Publishing Knoxville , 2009 .

[16]  Vipin Tyagi,et al.  A Review of ROI Image Retrieval Techniques , 2014, FICTA.

[17]  Vipin Tyagi,et al.  A novel technique for location independent object based image retrieval , 2017, Multimedia Tools and Applications.

[18]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

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

[20]  Savvas A. Chatzichristofis,et al.  Image moment invariants as local features for content based image retrieval using the Bag-of-Visual-Words model , 2015, Pattern Recognit. Lett..

[21]  Kin-Man Lam,et al.  Simultaneous Hallucination and Recognition of Low-Resolution Faces Based on Singular Value Decomposition , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[22]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[23]  LinLin Shen,et al.  Visual-Patch-Attention-Aware Saliency Detection , 2015, IEEE Transactions on Cybernetics.

[24]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[26]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[27]  Vipin Tyagi,et al.  Texture image retrieval using adaptive tetrolet transforms , 2016, Digit. Signal Process..

[28]  Jitendra Malik,et al.  Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.