Saliency Detection for Content Aware Computer Vision
Applications
Manipoonchelvi Pandivalavan and Muneeswaran Karuppiah
Department of computer science and engineering, Mepco Schlenk Engineering College, India
Abstract: 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.
Keywords: Content aware processing, saliency detection, computational visual attention.
Received June 12, 2014; accepted September 15, 2014
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