Automatic Person Annotation of Family Photo Album
Digital photographs are replacing tradition films in our daily life and the quantity is exploding. This stimulates the strong need for efficient management tools, in which the annotation of “who” in each photo is essential. In this paper, we propose an automated method to annotate family photos using evidence from face, body and context information. Face recognition is the first consideration. However, its performance is limited by the uncontrolled condition of family photos. In family album, the same groups of people tend to appear in similar events, in which they tend to wear the same clothes within a short time duration and in nearby places. We could make use of social context information and body information to estimate the probability of the persons' presence and identify other examples of the same recognized persons. In our approach, we first use social context information to cluster photos into events. Within each event, the body information is clustered, and then combined with face recognition results using a graphical model. Finally, the clusters with high face recognition confidence and context probabilities are identified as belonging to specific person. Experiments on a photo album containing over 1500 photos demonstrate that our approach is effective.

title: Multi-faceted contextual model for person identification in news video
abstract: Person identification is very important in the domain of multimedia news as it is often the focus of events in news stories and interest of searchers. However, this detection is impeded by the imprecise audio/visual analysis tools. In this paper, we describe a multimodal and multi-faceted approach to person-x detection in news video. We make use of multimodal features extracted from text, visual and audio inherent in news video. We also incorporate multiple external sources of news from Web and parallel news archives to extract location and temporal profile of the persons. We call this second source of information the multi-faceted context. The multimodal, multi-faceted information is then fused using a rank boosting approach. Experiments on TRECVID 2003 and 2004 search queries demonstrate that our approach is effective

title: Face Alignment with Unified Subspace Optimization of Active Statistical Models
abstract: Active statistical models including active shape models and active appearance models are very powerful for face alignment. They are composed of two parts: the subspace model(s) and the search process. While these two parts are closely correlated, existing efforts treated them separately and had not considered how to optimize them overall. Another problem with the subspace model(s) is that the two kinds of parameters of subspaces (the number of components and the constraints on the components) are also treated separately. So they are not jointly optimized. To tackle these two problems, an unified subspace optimization method is proposed. This method is composed of two unification aspects: (I) unification of the statistical model and the search process: the subspace models are optimized according to the search procedure; (2) unification of the number of components and the constraints: the two kinds of parameters are modelled in an unified way, such that they can be optimized jointly. Experimental results demonstrate that our method can effectively find the optimal subspace model and significantly improve the performance

title: Tour the world: a technical demonstration of a web-scale landmark recognition engine
abstract: We present a technical demonstration of a world-scale touristic landmark recognition engine. To build such an engine, we leverage ~21.4 million images, from photo sharing websites and Google Image Search, and around two thousand web articles to mine the landmark names and learn the visual models. The landmark recognition engine incorporates 5312 landmarks from 1259 cities in 144 countries. This demonstration gives three exhibits: (1) a live landmark recognition engine that can visually recognize landmarks in a given image; (2) an interactive navigation tool showing landmarks on Google Earth; and (3) sample visual clusters (landmark model images) and a list of 1000 randomly selected landmarks from our recognition engine with their iconic images.

title: Visual Synset: Towards a higher-level visual representation
abstract: We present a higher-level visual representation, visual synset, for object categorization. The visual synset improves the traditional bag of words representation with better discrimination and invariance power. First, the approach strengthens the inter-class discrimination power by constructing an intermediate visual descriptor, delta visual phrase, from frequently co-occurring visual word-set with similar spatial context. Second, the approach achieves better intra-class invariance power, by clustering delta visual phrases into visual synset, based their probabilistic dasiasemanticspsila, i.e. class probability distribution. Hence, the resulting visual synset can partially bridge the visual differences of images of same class. The tests on Caltech-101 and Pascal-VOC 05 dataset demonstrated that the proposed image representation can achieve good accuracies.

title: Regenerative Strategy for Fairness in Bi-directional Three-Node Relaying
abstract: The fairness solution without deteriorating the system sum-rate is a challenge under a total energy constraint. One regenerative strategy is proposed to improve the fairness for bi-directional three-node relaying, which is based on decode-and-forward technique with network coding and power optimization. In this letter, the application of network coding decreases the number of transmission phases from traditional four phases to three phases. Moreover, the proposed power optimization algorithm can be applied in practical system, which transforms max-min optimization problem to linear programming (LP) with low complexity. Numerical simulations shows this strategy enhances the minimum of unidirectional transmission rate up to 94% as compared to a four-phase bi-directional strategy, and up to 46% as compared to the three-phase bi-directional strategy with equal-power allocation.

title: MIMO Broadcast Transmission Strategy over Fast Time-Varying Channels
abstract: In time-varying channels, the channel state information available at the transmitter (CSIT) is outdated due to inherent time delay between the uplink channel estimation and the downlink data transmission in TDD systems. In this letter, we propose an iterative precoding method and a linear decoding method which are both based on minimum mean-squared error (MMSE) criteria to mitigate the interference among data streams and users created by outdated CSIT for multiuser MIMO downlink systems. Analysis and simulation results show that the proposed method can effectively reduce the impairment of the outdated CSIT and improve the system capacity.

title: Outage Probability Based Optimal Transmission of Space Time Block Codes over Correlated Distributed Antennas
abstract: Aiming to optimally transmit space-time block codes (STBCs) over distributed antennas (DAs), this paper examines downlink transmit antenna subset selection with power allocation for STBCs in non-ergodic Rayleigh fading channels with receive antenna correlations. Closed-form outage probability is first derived, which is a function of data rate, rate of STBCs, transmit power, large-scale fading (shadowing and path loss), power allocation weights to each DA and receive antenna correlation. However, achieving the optimal power allocation solution is computationally demanding and the use of sub-optimal techniques is necessitated. Assuming feedback of eigenvalues of transmit and receive antenna correlation matrix at the transmitter and accurate channel state information (CSI) at the receiver, an antenna subset selection with sub-optimal power allocation scheme is proposed, whose performance approaches optimal. The effectiveness of this sub-optimal method has been demonstrated by numerical results.

title: Distributed file system support for virtual machines in grid computing
abstract: This paper presents a data management solution which allows fast virtual machine (VM) instantiation and efficient run-time execution to support VMs as execution environments in grid computing. It is based on novel distributed file system virtualization techniques and is unique in that: 1) it provides on-demand access to VM state for unmodified VM monitors; 2) it supports user-level and write-back disk caches, per-application caching policies and middleware-driven consistency models; and 3) it supports the use of meta-data associated with files to expedite data transfers. The paper reports on its performance in a WAN setup using VMware-based VMs. Results show that the solution delivers performance over 30% better than native NFS and can bring application-perceived overheads below 10% relatively to a local disk setup. The solution also allows a VM with 1.6GB virtual disk and 320MB virtual memory to be cloned within 160 seconds when it is first instantiated (and within 25 seconds for subsequent clones).

title: Cooperative Autonomic Management in Dynamic Distributed Systems
abstract: The centralized management of large distributed systems is often impractical, particularly when the both the topology and status of the system change dynamically. This paper proposes an approach to application-centric self-management in large distributed systems consisting of a collection of autonomic components that join and leave the system dynamically. Cooperative autonomic components self-organize into a dynamically created overlay network. Through local information sharing with neighbors, each component gains access to global information as needed for optimizing performance of applications. The approach has been validated and evaluated by developing a decentralized autonomic system consisting of multiple autonomic application managers previously developed for the In-VIGO grid-computing system. Using analytical results from complex random network and measurements done in a prototype system, we demonstrate the robustness, self-organization and adaptability of our approach, both theoretically and experimentally.