Showing papers by "Yap-Peng Tan published in 2012"

Neighborhood repulsed metric learning for kinship verification

Abstract: Kinship verification from facial images is an interesting and challenging problem in computer vision, and there are very limited attempts on tackle this problem in the literature. In this paper, we propose a new neighborhood repulsed metric learning (NRML) method for kinship verification. Motivated by the fact that interclass samples (without a kinship relation) with higher similarity usually lie in a neighborhood and are more easily misclassified than those with lower similarity, we aim to learn a distance metric under which the intraclass samples (with a kinship relation) are pulled as close as possible and interclass samples lying in a neighborhood are repulsed and pushed away as far as possible, simultaneously, such that more discriminative information can be exploited for verification. To make better use of multiple feature descriptors to extract complementary information, we further propose a multiview NRML (MNRML) method to seek a common distance metric to perform multiple feature fusion to improve the kinship verification performance. Experimental results are presented to demonstrate the efficacy of our proposed methods. Finally, we also test human ability in kinship verification from facial images and our experimental results show that our methods are comparable to that of human observers.

Energy-Efficient Resource Allocation and Scheduling for Multicast of Scalable Video Over Wireless Networks

Abstract: In this paper, we investigate optimal resource allocation and scheduling for scalable video multicast over wireless networks. The wireless video multicasting is a best-effort service which has limited transmission energy and channel access time. To cater for multi-resolution videos to heterogeneous clients and for channel adaptation, we adopt scalable video coding (SVC) with spatial, temporal and quality scalabilities. Our scalable video multicast system consists of a channel probing stage to gather the channel state information and a transmission stage to multicast videos to clients. We formulate the optimal resource allocation problem by maximizing the video quality of the clients subject to transmission energy and channel access constraints. We show that the problem is a joint optimization of the selection of modulation and coding scheme (MCS), and the transmission power allocation. By imposing a quality-of-service (QoS) constraint on the packet loss rate, we simplify the original problem to a binary knapsack problem which can be solved by a dynamic programming approach. Specifically, we first propose a multicast scheduling scheme based on the quality impact of each SVC layer. Guided by the content-aware multicast scheduling, we optimize the resource allocation for each SVC layer sequentially. Solution at each step takes into account of the channel condition, remaining resources, and client requirements. The proposed scheme is of linear complexity and leads to the maximized video quality for the admitted clients, while satisfying the energy budget and channel access constraints. Experiment results demonstrate that our scheme achieves notable video quality improvements for multicast clients, when compared to the state-of-the-art video multicast method.

Cost-Sensitive Semi-Supervised Discriminant Analysis for Face Recognition

Abstract: This paper presents a cost-sensitive semi-supervised discriminant analysis method for face recognition. While a number of semi-supervised dimensionality reduction algorithms have been proposed in the literature and successfully applied to face recognition in recent years, most of them aim to seek low-dimensional feature representations to achieve low classification errors and assume the same loss from all misclassifications in the feature representation/extraction phase. In many real-world face recognition applications, however, this assumption may not hold as different misclassifications could lead to different losses. For example, it may cause inconvenience to a gallery person who is misrecognized as an impostor and not allowed to enter the room by a face recognition-based door locker, but it could result in a serious loss or damage if an impostor is misrecognized as a gallery person and allowed to enter the room. Motivated by this concern, we propose in this paper a new method to learn a discriminative feature subspace by making use of both labeled and unlabeled samples and exploring different cost information of all the training samples simultaneously. Experimental results are presented to demonstrate the efficacy of the proposed method.

Learning modality-invariant features for heterogeneous face recognition

Abstract: This paper addresses the problem of heterogeneous face recognition where the gallery and probe face samples are captured from two different modalities. Due to large discrepancies yet weak relationships across heterogeneous face image sets, most existing face recognition algorithms usually suffer from this application scenario. To address this problem, we propose in this paper to learn modality-invariant features (MIF) for heterogeneous face recognition. In our proposed method, a pair of heterogeneous face datasets are used as generic training datasets, and the relationship between both gallery and probe samples and generic training datasets are computed as modality-invariant features for matching heterogeneous face images. The rationale of our method is motivated by the fact the local geometrical information of each pair of heterogeneous face samples are usually similar in the corresponding generic training sets. Experimental results are presented to show the efficacy of the proposed method.

QoE-aware resource allocation for scalable video transmission over multiuser MIMO-OFDM systems

Abstract: We investigate in this paper how to maximize multiuser Quality of Experience (QoE) when scalable videos are transmitted over Multiple-Input Multiple-Output (MIMO)-Orthogonal Frequency Division Multiplexing (OFDM) systems. We first study the QoE issues in Scalable Video Coding (SVC) adaptation by constructing a QoE assessment database. We derive the optimal scalability adaptation track for individual video and further summarize common scalability adaptation tracks for grouped videos. A rate-model is developed for SVC adaptation and is employed in designing an efficient resource allocation solution for SVC streaming over multiuser MIMO-OFDM systems. Specifically, time-frequency unit assignment, power allocation, and modulation selection are jointly optimized to maximize users' QoE. Experimental results show that the proposed QoE-aware scalability adaptation scheme significantly outperforms the conventional adaptation schemes, and the proposed QoE-aware resource allocation achieves better QoE performance when compared to existing resource allocation methods.

QoE analysis for scalable video adaptation

Abstract: Quality of Experience (QoE) serves as a key service goal in video applications. In this paper, we study the QoE issue in scalable video adaptation by constructing a subjective video quality assessment database based on the full scalability of SVC. We derive the optimal scalability adaptation track for individual video and further summarize common scalability adaptation tracks for grouped videos. The common track provides useful guidelines on how to adapt scalable video based on their content characteristics. A rate-QoE model is proposed accordingly for the SVC adaptation. Experimental analyses show that the novel QoE-aware scalability adaptation scheme significantly outperforms the existing ones.

Video organization: Near-Duplicate Video clustering

Abstract: It is not uncommon to see several videos of almost identical content on the internet. These near duplicates, coupled with the sheer number of videos, pose a big challenge to the effective organization of video clips online. We propose an adaptive classification approach to detect near-duplicate versions, and an integrated voting strategy to group clusters and to elect a representative for each cluster. Our proposed methods are based on our observation that near-duplicate videos usually span a small, albeit variable area in the feature space, while videos of different contents are scattered far apart. The classification method aims to select a suitable threshold by maximizing the margin for each video sequence in the similarity space, and the voting scheme focuses on merging subsets with mutual information based on neighbor information and inverted indices. Experimental results on an unconstrained web dataset including over 10000 videos demonstrate the efficacy of the proposed methods.

A scalable resource allocation framework for SVC video transmissions over downlink MIMO-OFDM networks

Abstract: In this paper, we propose a scalable resource allocation framework for transmission of multiple scalable videos over downlink MIMO-OFDM networks. First, we analyze the rate-utility relationship of scalable video by a packet prioritization scheme, which ensures an optimized video utility under the rate constraint. The scalable framework is then proposed to achieve differentiated service objectives for different video layers. To provide every user with a fair opportunity to receive video for basic viewing, a MAXMIN fairness is designed to have their base layer video packets received. When all users have their base layer packets successfully received, the resources are distributed to exploit network efficiency. Resource allocations are accomplished by the efficient bit loading and power allocation for different optimization goals. The performance of the proposed scheme is validated by comparing with conventional resource allocation schemes.

Energy-minimized wireless video transmissions via sharing of retransmission limits

Abstract: In wireless video streaming, a video frame is usually large in data load and is truncated into multiple transport packets for reliable transmissions. These transport packets should be transmitted successfully before a deadline for frame decoding. High bitrate and high power transmission schemes are often deployed to ensure reliable transmissions. Such a method however incurs substantial energy consumption. Unlike existing methods which assign the retransmission limit independently to each transport packet, we share the retransmission opportunity among transport packets of a video frame, and jointly optimize it with transmission rate and power such that the video frame delivery is energy efficient. Contrary to the common intuition that avoids packet loss and retransmission to preserve energy, our approach yields lower energy consumption by judiciously inducing loss and retransmission of the transport packets, without violating the deadline and reliability constraint. Numerical results reveal that the joint optimization improves energy efficiency in wireless video transmissions. The improvement is even more significant when the allocated transmission time is limited or the reliability constraint is tightened.

Generalized subspace distance for set-to-set image classification

Abstract: Recent research in visual data classification often involves image sets and the measurement of dissimilarity between each pair of them. An effective solution is to model each image set using a subspace and compute the distance between these two subspaces as the dissimilarity between the sets. Several subspace similarity measures have been proposed in the literature. However, their relationships have not been well explored and most of them do not fully utilize the different importance of individual bases of each subspace. To consolidate this family of subspace-based measures, we propose a generalized subspace distance (GSD) framework and show that most existing subspace similarity measures can be considered as its special cases. To better utilize the different importance, we further propose a new fractional order weighted subspace distance (FOWSD) method within the GSD framework, by assigning different weights to the bases of each subspace and thus characterizing their different importance in similarity measurement. Experimental results on two image classification tasks including face recognition and object recognition are presented to show the effectiveness of the proposed method.