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In this paper, we propose a palmprint recognition scheme using histograms of sparse codes (HSC) as a new feature for palmprint image. In the feature extraction stage, the HSC feature is obtained by computing sparse codes for a given dictionary from a palmprint image, which results in a feature image. In the feature encoding stage, a hash table is designed from the feature image using the binary hashing...
Sparse representation based anomaly detection algorithms have received a widely interest in recent years. However, most of the existing approaches fail to pay attention to the manifold structure of the video data, which has been pointed to be important for data representation. To overcome this limitation, we develop a new sparse coding algorithm named constrained sparse representation (CSR) for video...
A graph is used to represent data in which the relationships between the objects in the data are at least as important as the objects themselves. Over the last two decades nearly a hundred file formats have been proposed or used to provide portable access to such data. This paper seeks to review these formats, and provide some insight to both reduce the ongoing creation of unnecessary formats, and...
We present a locality preserving K-SVD (LP-KSVD) algorithm for joint dictionary and classifier learning, and further incorporate kernel into our framework. In LP-KSVD, we construct a locality preserving term based on the relations between input samples and dictionary atoms, and introduce the locality via nearest neighborhood to enforce the locality of representation. Motivated by the fact that locality-related...
In order to deal with the huge amount of content generated by social media, especially for indexing and retrieval purposes, the focus shifted from single object recognition to multi-observation object recognition. Of particular interest is the problem of face recognition (used as primary cue for persons' identity assessment), since it is highly required by popular social media search engines like...
In this paper, we develop a new efficient graph construction algorithm that is useful for many learning tasks. Unlike the main stream for graph construction, our proposed data self-representativeness approach simultaneously estimates the graph structure and its edge weights through sample coding. Compared with the recent l1 graph that is based on sparse coding, our proposed objective function has...
Face recognition is of paramount importance in computer vision and biometrics systems. In this paper we propose an improved method which is suitable to handle variations in image configurations like pose, illumination, and facial expressions as well as occlusion and disguise, in order to provide high efficien-cyi in the face recognition. This method integrates the low-rank matrix which is recovered...
In this paper, we propose a new graph-based sparse coding and embedding (GSCE) method for activity-based human identification. Different from human activity recognition which recognizes different types of human activities such as walking, running, eating, and drinking, in this study, we aim to identify persons from his/her activities. To our best knowledge, this problem has been seldom investigated...
Matrix factorization techniques have been frequently utilized in pattern recognition and machine learning. Among them, Non-negative Matrix Factorization (NMF) has received considerable attention because it represents the naturally occurring data by parts of it. On the other hand, from the geometric perspective, the data is usually sampled from a low dimensional manifold embedded in a high dimensional...
The recent emerging sparse coding (SC) algorithms do not take local manifold structure of samples into consideration, while graph regularized sparse coding (GraphSC) algorithm only constrains the locality consistency of samples. Furthermore, the graph construction approach based on k-nearest-neighbor usually pre-defines the number of neighbors for all the samples, which may fails to fit the intrinsic...
We consider learning a discriminative dictionary in sparse representation and specifically focus on face recognition application to improve its performance. This paper presents an algorithm to learn a discriminative dictionary with low-rank regularization on the dictionary. To make the dictionary more discerning, we apply Fisher discriminant function to the coding coefficients with the goal that they...
Probabilistic latent semantic analysis is a topic modeling technique to discover the hidden structure in binary and count data. As a mixture model, it performs a probabilistic mixture decomposition on the co-occurrence matrix, which produces two matrices assigned with probabilistic explanations. However, the factorized matrices may be rather smooth, which means we may obtain global feature and topic...
In this paper, we employ a linguistic-processing approach to the content-based retrieval of music information. Central to the approach is the use of a lossy version of the Lempel-Ziv incremental parsing (LZIP) algorithm, which constructs a dictionary by incrementally parsing music feature vectors. LZIP is adopted as a source characterization technique owing to it's universal-coding nature, and asymptotic...
In this paper, we study the problem of segmenting tracked feature point trajectories of multiple moving objects in an image sequence. Using the affine camera model, this problem can be cast as the problem of segmenting samples drawn from multiple linear subspaces. In practice, due to limitations of the tracker, occlusions, and the presence of nonrigid objects in the scene, the obtained motion trajectories...
We present a new method for compact representation of large image datasets. Our method is based on treating small patches from an image as matrices as opposed to the conventional vectorial representation, and encoding those patches as sparse projections onto a set of exemplar orthonormal bases, which are learned a priori from a training set. The end result is a low-error, highly compact image/patch...
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