Multimedia content creation and manipulation have garnered attention in recent days due to the desires of personalization. As a content producing application, we propose a novel idea that requires the fusion of video and audio intelligence. The system is composed of at least three core techniques: 1) the capability to process the video sequence to have access to the geometric and appearance information pertaining to meaningful and representative targets, 2) a systematic way to reliably classify and identify important emotions from the music, 3) effective approaches to manipulate the video targets according to the extracted music emotions. In this paper, we report preliminary results of the proposed system. Specifically, we introduce the employed framework to manipulate the magnitude and speed of music conducting gestures of a video sequence of human skeleton according to the emotion intensity and tempo of an arbitrary music excerpt, using state-of-the-art inverse kinematics and music information retrieval techniques. We present the details of the prototype system and validate its effectiveness with a video demonstrating how we can manipulate the music conducting gestures according to the proposed manipulation rules.

In this article, we propose a novel personalized ranking system for amateur photographs. The proposed framework treats the photograph assessment as a ranking problem and we introduce the idea of personalized ranking , which ranks photographs considering both their aesthetic qualities and personal preferences. Photographs are described using three types of features: photo composition , color and intensity distribution , and personalized features . An aesthetic prediction model is learned from labeled photographs by using the proposed image features and RBF-ListNet learning algorithm. The experimental results show that the proposed framework outperforms in the ranking performance: a Kendall's tau value of 0.432 is significantly higher than those obtained by the features proposed in one of the state-of-the-art approaches (0.365) and by learning based on support vector regression (0.384). To realize personalization in ranking, three approaches are proposed: the feature-based approach allows users to select photographs with specific rules, the example-based approach takes the positive feedback from users to rerank the photograph, and the list-based approach takes both positive and negative feedback from users into consideration. User studies indicate that all three approaches are effective in both aesthetic and personalized ranking.

In this work, we focus on clustering faces in home photos by face recognition technologies. We propose two methods to improve the approach based on a well-known algorithm, local binary patterns. The adoption of the partial matching metric improves the recognition accuracy under face pose variations, while the adoption of the Gabor filter improves the accuracy under noises and various illuminations. We evaluate our methods on two home photo sets. In both evaluations, the results show that our methods improve the performance in accuracy. Compared to baseline LBP methods, in both evaluations, the results show that our methods improve the performance in accuracy from 90.4% to 99.5% and from 94.7% to 99.6% in two home-photo data sets respectively. Even compared to Google Picasa, the number of clusters where there is only one photo (thus can not be merged with other clusters, also means not good), our methods show that the number of "single" clusters reduced by half can be achieved. Our experience also shows that GPU speedup for Gabor filter can reach 140 times, and the overall system plus clustering can thus have 10 times speedup for face recognition.

In this paper, we propose a novel personalized ranking system for amateur photographs. Although some of the features used in our system are similar to previous work, new features, such as texture, RGB color, portrait (through face detection), and black-and-white, are included for individual preferences. Our goal of automatically ranking photographs is not intended for award-wining professional photographs but for photographs taken by amateurs, especially when individual preference is taken into account.

With the current widespread use of digital cameras, the process of selecting and maintaining personal photos is becoming an onerous task. To our knowledge, there has been little research on photo evaluation based on computational esthetics. Photographers around the world have established some general rules for taking good photos. Building upon artistic theories and human visual perception is difficult since the results tend to be subjective. Although automatically ranking award-wining professional photos may not be a sensible pursuit, such an approach may be reasonable for photos taken by amateurs. In the next section, we introduce rules for such a system.

Abstract In the Universal Mobile Telecommunications System (UMTS), session initiation protocol (SIP) and IPv6 are the default protocols for IP multimedia core network subsystem (IMS). However, a user equipment (UE) may not be allowed to roam or hand off from UMTS to a private‐IPv4 GPRS network. In this paper, we utilize SIP mobility and an automatic IPv6 tunneling mechanism, called Teredo, to support roaming/handoff of a UE between different networks. We have developed the first non‐commercial Linux‐based Teredo mechanism, and compared our solution with other Teredo implementations in the public domain. Our study indicates that our solution can reduce the tunneling overhead and transmission delay over two other implementations by 44–74%. Copyright © 2006 John Wiley & Sons, Ltd.