In recent years, there has been an increased scope for intelligent computer vision systems, which analyse the content of multimedia data. These systems are expected to process a huge quantum of image/data with high speed and without compromising on effectiveness. Such systems are benefited by reducing the amount of visual information by selectively processing only a relevant portion of the input data. The core issue in building these systems is to reduce irrelevant information and retain only a relevant subset of the input visual information. To address this issue, we propose a region-based computational visual attention model for saliency detection in images. The proposed model determines the salient object or part of the salient object without prior knowledge of its shape and color. The proposed framework has three components. First, the input image is segmented into homogeneous regions and then smaller regions are merged with neighbouring regions based on color and spatial distance between them. Second, three attributes such as spatial position, color contrast and size of each region are evaluated to distinguish salient object/parts of salient object. Finally, irrelevant background regions are suppressed and the region level saliency map is generated based on the three attributes. The generated saliency map preserves the shape and precise location of salient regions and hence it can be used to create high quality segmentation masks for high- level machine vision applications. Experimental results show that our proposed approach qualitatively better than the state-of- the-art approaches and quantitatively comparable to human perception.

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[17] Zhang Q., Gu G., and Xiao H., “Image Segmentation Based on Visual Attention Mechanism,” Journal of Multimedia, vol. 4, no. 6, pp. 363-370, 2009. Manipoonchelvi Pandivalavan received the bachelor of engineering degree in computer science and engineering from Bharathidasan University, Tamilnadu, India, in 1997, and the master of engineering degree in computer science and engineering from the Regional Engineering College, Tamilnadu, India, in 2001. She joined HCL Technologies, India, as Member Technical Staff in 2001 and left the company as Associate Project Manager in 2010. During her tenure at HCL Technologies she worked on image registration, rear view aid system for automobiles and traffic signal detection system. She is currently, pursuing Ph.D. degree at Department of Computer Science and Engineering in Mepco Schlenk Engineering College, India, affiliated to Anna University. Her current research interests include semantics based image segmentation, object tracking and, computer vision applications. Muneeswaran Karuppiah received the bachelor of engineering degree in Electronics and Communication engineering from Madurai Kamarajar University, Tamilnadu, India in 1984 and the master of engineering in computer science and engineering from Bharathiyar University, Tamilnadu, India, in 1990. In 2006, he received the Ph.D. degree in computer science engineering from M.S. University, Tamilnadu, India. He is in teaching and research for the past 28 years and 12 years respectively and currently, he is working as professor in Computer Science and Engineering Department at Mepco Schlenk Engineering College, Tamilnadu. His research interests are image processing, neural networks, and semantics analysis. He has authored or co-authored about 75 publications in journal/conference level and one book on compiler design.