Identification and ranking of relevant image content

In this paper, we present an image understanding algorithm for automatically identifying and ranking different image regions into several levels of importance. Given a color image, specialized maps for classifying image content namely: weighted similarity, weighted homogeneity, image contrast and memory colors are generated and combined to provide a metric for perceptual importance classification. Further analysis yields a region ranking map which sorts the image content into different levels of significance. The algorithm was tested on a large database of color images that consists of the Berkeley segmentation dataset as well as many other internal images. Experimental results show that our technique matches human manual ranking with 90% efficiency. Applications of the proposed algorithm include image rendering, classification, indexing and retrieval.

[1]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[2]  Hsien-Che Lee,et al.  Detecting boundaries in a vector field , 1991, IEEE Trans. Signal Process..

[3]  A. Murat Tekalp,et al.  Fusion of color and edge information for improved segmentation and edge linking , 1997, Image Vis. Comput..

[4]  Yang Hongying,et al.  A new regions-of-interest based image retrieval using DWT , 2005, IEEE International Symposium on Communications and Information Technology, 2005. ISCIT 2005..

[5]  Fred Stentiford,et al.  Visual attention for region of interest coding in JPEG 2000 , 2003, J. Vis. Commun. Image Represent..

[6]  Xu De,et al.  Region-based image retrieval using color coherence region vectors , 2004, Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004..

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

[8]  King Ngi Ngan,et al.  Unsupervised extraction of visual attention objects in color images , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Eli Saber,et al.  Unsupervised color image segmentation using a dynamic color gradient thresholding algorithm , 2008, Electronic Imaging.

[10]  Fred Stentiford,et al.  An evolutionary programming approach to the simulation of visual attention , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[11]  Sridha Sridharan,et al.  Importance assignment to regions in surveillance imagery to aid visual examination and interpretation of compressed images , 2001, Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No.01EX489).

[12]  Meir Feder,et al.  Image compression via improved quadtree decomposition algorithms , 1994, IEEE Trans. Image Process..

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

[14]  Michael T. Goodrich,et al.  Algorithm Design: Foundations, Analysis, and Internet Examples , 2001 .

[15]  A. Murat Tekalp,et al.  Automatic Image Annotation Using Adaptive Color Classification , 1996, CVGIP Graph. Model. Image Process..

[16]  Alvaro Pardo.  Extraction of semantic objects from still images , 2002, Proceedings. International Conference on Image Processing.

[17]  Ki Tae Park,et al.  Automatic Extraction of Salient Objects using Feature Maps , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[18]  Christof Koch,et al.  Feature combination strategies for saliency-based visual attention systems , 2001, J. Electronic Imaging.

[19]  Hyeran Byun,et al.  SVM-based salient region(s) extraction method for image retrieval , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[20]  Fred Stentiford,et al.  An estimator for visual attention through competitive novelty with application to image compression , 2001 .

[21]  B. S. Manjunath,et al.  Unsupervised Segmentation of Color-Texture Regions in Images and Video , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Ling Guan,et al.  Characterization of perceptual importance for object-based image segmentation , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[23]  David Jeff Jackson,et al.  Improved quadtree decomposition/recomposition algorithm for fractal image compression , 1999, Proceedings IEEE Southeastcon'99. Technology on the Brink of 2000 (Cat. No.99CH36300).

[24]  Fred Stentiford,et al.  Automatic identification of regions of interest with application to the quantification of DNA damage in cells , 2002, IS&T/SPIE Electronic Imaging.

[25]  Antonio Torralba,et al.  Modeling global scene factors in attention. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[26]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[27]  Claudio M. Privitera,et al.  Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Anthony J. Maeder,et al.  Automatic identification of perceptually important regions in an image , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[29]  KeDai Zhang,et al.  Automatic Salient Regions of Interest Extraction Based on Edge and Region Integration , 2006, 2006 IEEE International Symposium on Industrial Electronics.

[30]  Oge Marques,et al.  Using visual attention to extract regions of interest in the context of image retrieval , 2006, ACM-SE 44.

[31]  Neil W. Bergmann,et al.  An automatic image quality assessment technique incorporating higher level perceptual factors , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).