Abstract
One of the most important and essential parts of image processing tasks in computer vision is to predict the regions of interest that is the most attractive and also representative salient ones. This task can be realized effortless by the human visual system via the function of selective attention. In this paper, we introduced a set of new visual features, including contract, entropy, and local feature change into the Itti bottom-up model. We used a set of qualitative and quantitative analysis to demonstrate the effectiveness of the proposed approach on a large dataset with different scenes, and the results show that the modified Itti model combined with the new features can improve greatly the efficiency of salient region detection in real scenes.
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Li, S., Li, Y. (2012). A Modified Selective Attention Model for Salient Region Detection in Real Scenes. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_29
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DOI: https://doi.org/10.1007/978-3-642-33506-8_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
Online ISBN: 978-3-642-33506-8
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