A novel image retrieval technique using automatic and interactive segmentation

In this paper, we present a new region-based image retrieval technique based on robust image segmentation. Traditional content-based image retrieval deals with the global description of a query image. We combine the state-of-the-art segmentation algorithms with the traditional approach to narrow the area of interest to a specific region within a query image. In case of automatic segmentation, the algorithm divides a query image automatically and computes Zernike moments for each region. For interactive segmentation, our proposed scheme takes as input a query image and some information regarding the region of interest. The proposed scheme then works by computing the Geodesic-based segmentation of the query image. The segmented image is our region of interest which is then used for computing the Zernike moments. The Euclidean distance is then used to retrieve different relevant images. The experimental results clearly show that the proposed scheme works efficiently and produces excellent results.

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

[2]  Kobus Barnard,et al.  Method for comparing content based image retrieval methods , 2003, IS&T/SPIE Electronic Imaging.

[3]  Stefan B. Williams,et al.  Reduced SIFT Features For Image Retrieval And Indoor Localisation , 2004 .

[4]  Bhuvana Shanmugam,et al.  An efficient perceptual of CBIR system using MIL-SVM classification and SURF feature extraction , 2017, Int. Arab J. Inf. Technol..

[5]  Guojun Lu,et al.  A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval , 2003, J. Vis. Commun. Image Represent..

[6]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[7]  Whoi-Yul Kim,et al.  A region-based shape descriptor using Zernike moments , 2000, Signal Process. Image Commun..

[8]  Karthikeyan Marappan,et al.  Efficient color and texture feature extraction technique for content based image retrieval system , 2016, Int. Arab J. Inf. Technol..

[9]  Mohamed A. Deriche,et al.  Robust image segmentation based on convex active contours and the Chan Vese model , 2014, 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[10]  Ji Wan,et al.  Deep Learning for Content-Based Image Retrieval: A Comprehensive Study , 2014, ACM Multimedia.

[11]  David Zhang,et al.  Automatic Image Segmentation by Dynamic Region Merging , 2010, IEEE Transactions on Image Processing.

[12]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Oded Maron,et al.  Multiple-Instance Learning for Natural Scene Classification , 1998, ICML.

[14]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Larry S. Davis,et al.  Improved fast gauss transform and efficient kernel density estimation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[16]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Guillermo Sapiro,et al.  Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting , 2009, International Journal of Computer Vision.

[18]  Mohamed A. Deriche,et al.  Color image segmentation by combining the convex active contour and the Chan Vese model , 2017, Pattern Analysis and Applications.

[19]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[20]  Hui Zhang,et al.  Localized Content-Based Image Retrieval , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[22]  Xiaojun Wu,et al.  A novel contour descriptor for 2D shape matching and its application to image retrieval , 2011, Image Vis. Comput..

[23]  Jen-Hao Hsiao,et al.  Deep learning of binary hash codes for fast image retrieval , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[24]  Guojun Lu,et al.  Shape-based image retrieval using generic Fourier descriptor , 2002, Signal Process. Image Commun..

[25]  Yi-Ping Hung,et al.  Region-based image retrieval using color-size features of watershed regions , 2009, J. Vis. Commun. Image Represent..