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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. [ABSTRACT FROM PUBLISHER]

Cloud computing is an emerging technology developed for providing various computing and storage services over the Internet. In this paper, we proposed a privacy-preserving cloud-aware scenario for compressive multimedia applications, including multimedia compression, adaptation, editing/manipulation, enhancement, retrieval, and recognition. In the proposed framework, we investigate the applicability of our/existing compressive sensing (CS)-based multimedia compression and securely compressive multimedia “trans-sensing” techniques based on sparse coding for securely delivering compressively sensed multimedia data over a cloud-aware scenario. Moreover, we also investigate the applicability of our/existing sparse coding-based frameworks for several multimedia applications by leveraging the strong capability of a media cloud. More specifically, to consider several fundamental challenges for multimedia cloud computing, such as security and network/device heterogeneities, we investigate the applications of CS and sparse coding techniques in multimedia delivery and applications. As a result, we can build a unified cloud-aware framework for privacy-preserving multimedia applications via sparse coding. [ABSTRACT FROM PUBLISHER]

Given the increasing number of mobile platforms, a key technical challenge is how to provide efficient image processing application on resource-limited mobile devices. This paper proposes a novel technique for mobile image abstraction on mobile platform. In order to reduce the computation complexity, the image abstraction process is conducted on a cloud computing system. In this technique, captured images are analyzed to detect visual salient area, which is then provided for adaptive detail preservation of abstraction. To further reduce the image size for efficient transmission between mobile device and cloud computing system while maintaining the visual quality of abstracted image, 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 efficacy of the proposed system for conducting image abstraction on mobile platform using sparse representation. [ABSTRACT FROM PUBLISHER]