Digital content consumption is exploding thanks to the advances of the distributed cloud-computing infrastructures and the consumer electronics. Further challenges have been posed to engineers and researchers to satisfy the ever increasing user needs not only for high quality video delivery, but also for richer experience. In order to support various video analysis tasks in addition to transcoding, a software platform is designed based on the Google cloud computing infrastructure with the features to be flexible, scalable, robust, and secure. In this paper, we discuss the scope, requirements, constraints, features of such a system, the problems we met and how they are resolved.

This paper proposes an adaptive color feature extraction scheme by considering the color distribution of an image. Based on the binary quaternion-moment-preserving (BQMP) thresholding technique, the proposed extraction methods, fixed cardinality (FC) and variable cardinality (VC), are able to extract color features by preserving the color distribution of an image up to the third moment and to substantially reduce the distortion incurred in the extraction process. In addition to utilizing the earth mover's distance (EMD) as the distance measure of our color features, we also devise an efficient and effective distance measure, comparing histograms by clustering (CHIC). Moreover, the efficient implementation of our extraction methods is explored. With slight modification of the BQMP algorithm, our extraction methods are equipped with the capability of exploiting the concurrent property of hardware implementation. The experimental results show that our hardware implementation can achieve approximately a second order of magnitude improvement over the software implementation. It is noted that minimizing the distortion incurred in the extraction process can enhance the accuracy of the subsequent various image applications, and we evaluate the meaningfulness of the new extraction methods by the application to content-based image retrieval (CBIR). Our experimental results show that the proposed extraction methods can enhance the average retrieval precision rate by a factor of 25% over that of a traditional color feature extraction method.

Image classification on personal photo collections can be extremely useful to various management tasks. However, there is little progress made forwarding it due to (1) lack of training data, and (2) subjectivity inherent in a user's photo-organizing behavior. In this paper, we propose a framework, user-assisted image classification on personal photo collections (UCP), to address this problem. The uniqueness of this framework is that it is user-centric and includes users in the loop. Our experimental results show that the techniques used in this framework are promising