Visual saliency based mobile images categorization using sparse representation on cloud computing

Given the increasing number of mobile platforms, a key technical challenge is how to provide an optimal photo browsing experience given the limited screen size available on mobile devices. This paper proposes a novel technique for intelligent mobile image categorization on mobile platform to reduce...

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Bibliographic Details
Main Authors: Duan-Yu Chen, Meng-Kai Hsieh, Jung-Hsi Lee
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Dictionaries
Image reconstruction
Mobile communication
Mobile handsets
Mobile Image Categorization
Sparse Coding
Vectors
Visual Saliency
Visualization
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Summary:Given the increasing number of mobile platforms, a key technical challenge is how to provide an optimal photo browsing experience given the limited screen size available on mobile devices. This paper proposes a novel technique for intelligent mobile image categorization on mobile platform to reduce computation complexity based on cloud computing. In this technique, captured images are analyzed to detect visual salient area, which is then classified in real-time using sparse representation. Mathematically, the derived algorithm regards the salient regions as the dictionary in sparse representation, and selects the salient regions that minimize the residual output error iteratively, thus the resulting regions have a direct correspondence to the performance requirements of the given problem. Experimental results obtained using extensive datasets captured under uncontrolled conditions show the proposed system effectively manages mobile images using sparse representation on cloud computing.
DOI:10.1109/ISIC.2012.6449748