With the rapid emergence of 3D displays, we can enrich the user's viewing experiences by adding depth information to the widely existing 2D contents. However, effectively inferring the associated depth from a single 2D image is still a challenging problem. By taking benefits from the recently appeared image descriptors, we proposed the use of an SVM based framework for addressing the single image depth estimation. One advantage is its direct extension to incorporate the recent researches of large scale classification via SVM to meet the upcoming cloud computing paradigm. Our experimental results showed that the proposed framework outperforms the state-of-the-art approaches in performance, even the ones using more complex graphical models like MRF. Also, we made a brief investigation on the individual effectiveness of a set of commonly used image descriptors and found that spatial descriptors (e.g. texture) would be more effective than frequency ones (e.g. DCT coefficients).

Observing the widespread use of Kinect-like depth cameras, in this work, we investigate into the problem of using sole depth data for human action recognition and retrieval in videos. We proposed the use of simple depth descriptors without learning optimization to achieve promising performances as compatible to those of the leading methods based on color images and videos, and can be effectively applied for real-time applications. Because of the infrared nature of depth cameras, the proposed approach will be especially useful under poor lighting conditions, e.g. the surveillance environments without sufficient lighting. Meanwhile, we proposed a large Depth-included Human Action video dataset, namely DHA, which contains 357 videos of performed human actions belonging to 17 categories. To the best of our knowledge, the DHA is one of the largest depth-included video datasets of human actions.

Human action video search is a frequent demand in multimedia applications, and conventional video search schemes based on keywords usually fail to correctly find relevant videos due to noisy video tags. Observing the widespread use of Kinect-like depth cameras, we propose to search human action videos by directly performing the target action with body movements. Human actions are captured by Kinect and the recorded depth information is utilized to measure the similarity between the query action and each human action video in the database. We use representative depth descriptors without learning optimization to achieve real-time and promising performance as compatible as those of the leading methods based on color images and videos. Meanwhile, a large Depth-included Human Action video dataset, namely DHA, is collected to prove the effectiveness of the proposed video search system.

The prevalence of augmented reality devices in our daily lives offers increasing opportunities for users to navigate the real world. However, as users move, on-screen targets move unpredictably, and eventually disappear from the screen in mobile navigation scenarios. The changing target movement pattern creates difficulty for users in selecting the targets on time before targets escape from the screen. This study proposes a novel target selecting technique, AttachedShock, for easing target selection tasks on augmented reality devices by crossing a naturally expanding wave pattern that is attached to targets. We evaluated the effectiveness of the proposed technique by conducting comparative studies on measuring the performance of four techniques under various mobile navigation scenarios. The results indicate that the proposed technique assists users in selecting moving targets to improve the error rate substantially, by a minimum of 61.75%, and incurs acceptable distractions to users, compared to other techniques.

Narrative video analysis has attracted much research attention, for narrative scenes can provide meaningful representations of multimedia contents. To go beyond the limitations of content based appraoches, social network techniques was introduced in the literature to explore the high-level narrative structures by mining the relations between video characters. Taking into account the fact that such a social network is not static but changes over time as the video narrative evolves, in this work, we develop a novel social network model, namely dynamic social network, for capturing the spatiotemporal dynamics in the social network of video characters so as to enable the automatic segmentation of a video into a sequence of narrative scenes. The proposed approach is experimented with various genres of movies and the results demonstrate our effectiveness.

This paper proposes an effective approach to provide relevant search terms for conceptual Web search. 'Semantic Term Suggestion' function has been included so that users can find the most appropriate query term to what they really need. Conventional approaches for term suggestion involve extracting frequently occurring key terms from retrieved documents. They must deal with term extraction difficulties and interference from irrelevant documents. In this paper, we propose a semantic term suggestion function called Collective Intelligence based Term Suggestion (CITS). CITS provides a novel social-network based framework for relevant terms suggestion with a semantic graph of the search term without limiting to the specific query term. A visualization of semantic graph is presented to the users to help browsing search results from related terms in the semantic graph. The search results are ranked each time according to their relevance to the related terms in the entire query session. Comparing to two popular commercial search engines, a user study of 18 users on 50 search terms showed better user satisfactions and indicated the potential usefulness of proposed method in real-world search applications.