Two paper have been accepted by IEEE SMC2013

Discriminant Multi-Component Face Analysis

Hongli Liu, Weifeng Liu*, Yanjiang Wang

Abstract—Sparse representation based classification (SRC) has attracted much attention in face analysis such as and . Currently, most of SRC based methods treated face as a whole component which results in under-utilization of the complementary in different facial parts. In this paper, we present an approach which can effectively explore the complementary of different facial parts to boost the performance of face analysis. In particular, we employ multi-view sparse coding techniques to learn the factorized representation of different facial components. Furthermore, we incorporate label information into the objective function to enforce the discriminability. To evaluate the performance, we conduct face analysis experiments including FR and FER on JAFFE database. Experimental results demonstrate that the proposed method can significantly boost the performance of face analysis.

Self-explanatory Convex Sparse Representation for Image classification

Baodi Liu, Yuxiong Wang, Bin Shen, Yujin Zhang, Yanjiang Wang, Weifeng Liu

Abstract-Sparse representation technique has been widely used in various areas of computer vision over the last decades. Unfortunately, in the current formulations, there are no explicit relationship between the learned dictionary and the original data. By tracing back and connecting sparse representation with the $K$-means algorithm, a novel variation scheme termed as self-explanatory convex sparse representation (SCSR) has been proposed in this paper. To be specific, the basis vectors of the dictionary are refined as convex combination of the data points. The atoms now would capture a notion of centroids similar to K-means, leading to enhanced interpretability. Sparse representation and K-means are thus unified under the same framework in this sense. Besides, an appealing property also emerges that the weight and code matrices both tend to be naturally sparse without additional constraints. Compared with the standard formulations, SCSR is easier to be extended into the kernel space. To solve the corresponding sparse coding subproblem and dictionary learning subproblem, block-wise coordinate descent and Lagrange multipliers are proposed accordingly. To validate the proposed algorithm, it is implemented in image classification, a successful applications of sparse representation. Experimental results on several benchmark data sets, such as UIUC-Sports, Scene 15, and Caltech-256 demonstrate the effectiveness of our proposed algorithm.

IEEE SMC2013 Deadline Extension

Deadline Extension: Due to numerous requests the submission deadline of SMC2013 has been extended to May 15, 2013.

http://www.smc2013.org/sites/default/files/c02_cfp.pdf

http://www.smc2013.org/

Special Session Call for Papers

SMC2013Special Session on Matrix and Tensor Analysis for Big Vision

Introduction/Call for Papers

Matrix and Tensor analysis has important applications in natural sciences, medicine, economics, engineering as well as in industry.The research of matrix and tensor analysis plays important roles in dimension reduction, spectral analysis, manifold learning, kernel machines, sparse coding, etc. In recent years, the matrix and tensor analysis presents new research opportunities to large scale visual data, e.g. large scale face recognition, intelligent visual surveillance, web-scale image retrieval/annotation /classification, massive object recognition etc. As a consequence, matrix and tensor analysis is a never-ending resilience field and attracts growing efforts from different fields.

This special session hunts for original research results for Matrix and Tensor Analysis for Big Vision. The goals of this special session are twofold: 1) developing matrix and tensor analysis algorithms to target specific applications in large-scale visual data analytics and 2) defining novel large-scale visual data driven applications, which can be cleared up by conventional matrix and tensor analysis algorithms.

Indicative Topics/Areas

Manuscripts are solicited to address a wide range of topics in matrix and tensor analysis, but not limit to the following:

Extension of traditional matrix and tensor analysis, multiview dimension reduction, spectral analysis, matrix and tensor analysis for manifold learning, kernel machines and tensor machines, matrix and tensor analysis for biometrics, matrix and tensor analysis for web-scale multimedia information retrieval, matrix and tensor analysis for video surveillance, matrix and tensor analysis for sparse analysis, etc.

Submission

Manuscripts for a Special Session should NOT be submitted in duplication to any other regular or special sessions and should be submitted to SMC 2013 main conference online submission system on SMC 2013 conference website.

All submitted papers of Special Sessions have to undergo the same review process (three completed reviews per paper). The technical reviewers for each Special Session paper will be members of the SMC 2013 Program Committee and qualified peer-reviewers to be nominated by the Special Session organizers.

Organized by IEEE SMC TC on Cognitive Computing

TIP paper "Multiview Hessian Regularization for Image Annotation" is online now.

Multiview Hessian Regularization for Image Annotation

Abstract

The rapid development of computer hardware and Internet technology makes large scale data dependent models computationally tractable, and opens a bright avenue for annotating images through innovative machine learning algorithms. Semi-supervised learning (SSL) has consequently received intensive attention in recent years and has been successfully deployed in image annotation. One representative work in SSL is Laplacian Regularization (LR), which smoothes the conditional distribution for classification along the manifold encoded in the graph Laplacian, however, it has been observed that LR biases the classification function towards a constant function which possibly results in poor generalization. In addition, LR is developed to handle uniformly distributed data (or single view data), although instances or objects, such as images and videos, are usually represented by multiview features, such as color, shape and texture. In this paper, we present multiview Hessian Regularization (mHR) to address the above two problems in LR-based image annotation. In particular, mHR optimally combines multiple Hessian regularizations, each of which is obtained from a particular view of instances, and steers the classification function which varies linearly along the data manifold. We apply mHR to kernel least squares and support vector machines as two examples for image annotation. Extensive experiments on the PASCAL VOC'07 dataset validate the effectiveness of mHR by comparing it with baseline algorithms, including LR and HR.

Index Terms

Image annotation, semi-supervised learning, manifold learning, Hessian, multiview learning.

pdf link: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6490057&isnumber=4358840