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Salient region detection using efficient wavelet-based textural feature maps

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Abstract

A salient region is part of an image that captures the highest level of attention by the human visual system. In this paper, a new salient region detection method is proposed by linearly combining the feature maps for the L, a and b color channels. Since, the wavelet transform is capable of providing a multi-scale spatial-frequency decomposition of the image, the color feature maps are obtained using this transform. A scheme is proposed whereby the channel feature maps are linearly combined. The weights for the linear combination are determined by making use of the entropy of the channel feature maps and a Gaussian kernel, utilizing the fact that the salient objects are generally clustered and scene-centric. The salient region is further refined by making use of the proximity of the pixels to the centers of gravity in the image feature map. Extensive simulations are conducted in order to evaluate the performance of the proposed saliency detection scheme by applying it to the natural images from several datasets. Experimental results show that the proposed method provides values of precision, recall and F-measure larger than and that of the mean absolute error smaller than those provided by other existing methods. The performance of the proposed salient region detection method is also evaluated on noisy images and it is shown to be robust against noise.

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Acknowledgements

This work was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada and in part by the Regroupement Strategique en Microelectronique du Quebec (ReSMiQ).

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  1. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada

    Masoumeh Rezaei Abkenar & M. Omair Ahmad

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  1. Masoumeh Rezaei Abkenar
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  2. M. Omair Ahmad
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Correspondence to M. Omair Ahmad.

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Rezaei Abkenar, M., Ahmad, M.O. Salient region detection using efficient wavelet-based textural feature maps. Multimed Tools Appl 77, 16291–16317 (2018). https://doi.org/10.1007/s11042-017-5199-3

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  • DOI: https://doi.org/10.1007/s11042-017-5199-3

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