Multi-scale saliency detection using dictionary learning

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dc.contributor.author Pachori, Shubham
dc.contributor.author Raman, Shanmuganathan
dc.date.accessioned 2016-11-29T00:06:15Z
dc.date.available 2016-11-29T00:06:15Z
dc.date.issued 2016-11
dc.identifier.citation Pachori, Shubham and Raman, Shanmuganathan, “Multi-scale saliency detection using dictionary learning”, arXiv, Cornell University Library, DOI: arXiv:1611.06307, Nov. 2016. en_US
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/2555
dc.identifier.uri https://arxiv.org/abs/1611.06307
dc.description.abstract Saliency detection has drawn a lot of attention of researchers in various fields over the past several years. Saliency is the perceptual quality that makes an object, person to draw the attention of humans at the very sight. Salient object detection in an image has been used centrally in many computational photography and computer vision applications like video compression, object recognition and classification, object segmentation, adaptive content delivery, motion detection, content aware resizing, camouflage images and change blindness images to name a few. We propose a method to detect saliency in the objects using multimodal dictionary learning which has been recently used in classification and image fusion. The multimodal dictionary that we are learning is task driven which gives improved performance over its counterpart (the one which is not task specific). en_US
dc.description.statementofresponsibility by Shubham Pachori and Shanmuganathan Raman
dc.language.iso en_US en_US
dc.publisher Cornell University Library en_US
dc.title Multi-scale saliency detection using dictionary learning en_US
dc.type Article en_US


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