Markovian mixture face recognition with discriminative face alignment
A typical automatic face recognition system is composed of three parts: face detection, face alignment and face recognition. Conventionally, these three parts are processed in a bottom-up manner: face detection is performed first, then the results are passed to face alignment, and finally to face recognition. The bottom-up approach is one extreme of vision approaches. The other extreme approach is top-down. In this paper, we proposed a Markovian stochastic mixture approach for combining bottom-up and top-down face recognition: face recognition is performed from the results of face alignment in a bottom-up way, and face alignment is performed based on the results of face recognition in a top-down way. By modeling the mixture face recognition as a stochastic process, the recognized person is decided probabilistically according to the probability distribution coming from the stochastic face recognition, and the recognition problem becomes that ldquowho the most probable person is when the stochastic process of face recognition goes on for an infinite long durationrdquo. This problem is solved with the theory of Markov chains by properly modeling the stochastic process of face recognition as a Markov chain. As conventional face alignment is not suitable for this mixture approach, discriminative face alignment is proposed. And we also prove that the Markovian mixture face recognition results only depend on discriminative face alignment, not on conventional face alignment. Our approach can surprisingly outperform the face recognition performance with manual face localization, which is demonstrated by extensive experiments.

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: 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: Automatic Person Annotation of Family Photo Album
abstract: 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: Exploring knowledge of sub-domain in a multi-resolution bootstrapping framework for concept detection in news video
abstract: In this paper, we present a model based on a multi-resolution, multi-source and multi-modal (M3) bootstrapping framework that exploits knowledge of sub-domains for concept detection in news video. Because the characteristics and distributions of data in different sub-domains are different, we model and analyze the video in each sub-domain separately using a transductive framework. Along with this framework, we propose a "pseudo-Vapnik combined error bound" to tackle the problem of imbalanced distribution of training data in certain segments of sub-domains. For effective fusion of multi-modal features, we utilize multi-resolution inference and constraints to permit evidences from different modal features to support each other. Finally, we employ a bootstrapping technique to leverage unlabeled data to boost the overall system performance. We test our framework by detecting semantic concepts in the TRECVID 2004 dataset. Experimental results demonstrate that our approach is effective.

title: Subspace analysis and optimization for AAM based face alignment
abstract: Active appearance models (AAM) is very powerful for extracting objects, e.g. faces, from images. It is composed of two parts: the AAM subspace model and the AAM search. While these two parts are closely correlated, existing efforts treated them separately and had not considered how to optimize them overall. In this paper, an approach is proposed to optimize the subspace model while considering the search procedure. We first perform a subspace error analysis, and then to minimize the AAM error we propose an approach which optimizes the subspace model according to the search procedure. For the subspace error analysis, we decomposed the subspace error into two parts, which are introduced by the subspace model and the search procedure respectively. This decomposition shows that the optimal results of AAM can be achieved only by optimizing both of them jointly rather than separately. Furthermore, based on this error decomposition, we develop a method to find the optimal subspace model according to the search procedure by considering both the two decomposed errors. Experimental results demonstrate that our method can find the optimal AAM subspace model rapidly and improve the performance of AAM significantly.

title: Limited-Feedback Modified Block Diagonalization for Multiuser MIMO Downlink with Time-Varying Channels
abstract: Block diagonalization (BD) is a low-complexity linear precoding scheme for multiuser MIMO (MU-MIMO) down-link, which can completely eliminate multi-user interference with perfect channel state information (CSI) at the transmitter. Under the assumption of block fading channels, BD with fixed amount of feedback is interference-limited. In this paper, we first introduce a low-complexity CSI feedback scheme, by exploiting the temporal correlation of practical channels, to improve the efficiency of feedback. Then, a modified BD algorithm is proposed to further utilize the channel correlation in time domain. Combined with the CSI feedback scheme, the modified BD algorithm handles the interference-limiting problem in a wide range of SNR with a fixed, small number of feedback bits.

title: On the Performance Analysis of Network-Coded Cooperation in Wireless Networks
abstract: In this letter, a network-coded cooperation scheme with dynamic coding mechanism (DC-NCC) is proposed. In DC-NCC, the relay dynamically adapts forming the network-coded data based on the observed instantaneous source-to-relay channel quality, and then forwards the network-coded data towards corresponding destinations. Under the assumption (denoted as A) that each destination can reliably overhear the data from other sources, the diversity-multiplexing tradeoff of DC-NCC is proved to outperform that of conventional cooperation (CC), This verifies that DC-NCC outperforms CC in bandwidth efficiency. Moreover, DC-NCC offers reduced system outage probability and single-pair outage probability compared with CC, and achieves the same full diversity order as CC at high signal-to-noise ratio (SNR). Numerical results also show that with dynamic coding, DC-NCC avoids unnecessary error propagation which can be caused by coding those erroneous data into network-coded data. Finally the performance of DC-NCC is discussed in case the assumption A is removed. For a wireless network composed of N source-destination (s-d) pairs and a single relay node, although there is a certain outage probability increase for each s-d pair, DC-NCC still achieves a diversity-multiplexing tradeoff superior to CC.

title: Joint Effects of Radio Channels and Node Mobility on Link Dynamics in Wireless Networks
abstract: In this paper, we study link properties over dynamic radio channels based on analytical models and simulations. Specifically, channel variability and mobility are investigated through two quantities: effective transmission range and node-pair distance, respectively. We find that the PDF of link lifetime can be approximated by exponential distribution with parameter characterized by the ratio of average node speed to effective transmission range. Moreover, we show that average link lifetime for slower mobile nodes is mainly determined by radio channel characteristics, whereas for faster mobile nodes, it is dominated by node mobility. Through analysis and simulations, we find that the impacting factors on residual link lifetime are in the decreasing order of average node speed, effective transmission range, and node-pair distance on the fly. We further present the implication and application of link properties to path lifetime, network connectivity, and routing performance.

title: SNCC: A Selective Network-Coded Cooperation Scheme in Wireless Networks
abstract: Network-coded cooperation is a new communication paradigm that exploits spatial diversity by pooling distributed "communication" (relay) and also the distributed "computation" resources (coding) of different nodes in a network so as to increase wireless networks system performance. In this paper, we develop a selective network-coded cooperation (SNCC) scheme suitable for multiple unicast transmission pairs in a single-cell wireless network. The relay node performs network coding on received information from multiple sources before forwarding the network-coded data towards the corresponding destinations, which can decode their desired data from the network-coded data and the data they overhear from the other sources. The performance analysis and evaluation in terms of the single-pair outage probability and diversity-multiplexing tradeoff show that under SNCC any transmission pair achieves a full diversity order of M+1 (M is the number of available relays) while holding a better diversity-multiplexing tradeoff performance than existing cooperative cooperation schemes. Moreover, due to the mechanism that the relay adapts the data on which to perform network coding according to the observed source-to-relay channel quality, SNCC smartly avoids error propagation due to incorporating erroneous data into network-coded data, which is verified by numerical results of the single-pair outage probability.