Discriminative Dictionary Learning Algorithm Research Articles

Multi-modal discriminative dictionary learning for Alzheimer's disease and mild cognitive impairment

Background and objectiveThe differentiation of mild cognitive impairment (MCI), which is the prodromal stage of Alzheimer's disease (AD), from normal control (NC) is important as the recent research emphasis on early pre-clinical stage for possible disease abnormality identification, intervention and even possible prevention. MethodsThe current study puts forward a multi-modal supervised within-class-similarity discriminative dictionary learning algorithm (SCDDL) we introduced previously for distinguishing MCI from NC. The proposed new algorithm was based on weighted combination and named as multi-modality SCDDL (mSCDDL). Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET) and florbetapir PET data of 113 AD patients, 110 MCI patients and 117 NC subjects from the Alzheimer's disease Neuroimaging Initiative database were adopted for classification between MCI and NC, as well as between AD and NC. ResultsAdopting mSCDDL, the classification accuracy achieved 98.5% for AD vs. NC and 82.8% for MCI vs. NC, which were superior to or comparable with the results of some other state-of-the-art approaches as reported in recent multi-modality publications. ConclusionsThe mSCDDL procedure was a promising tool in assisting early diseases diagnosis using neuroimaging data.

Discriminative dictionary pair learning based on differentiable support vector function for visual recognition

Sparse representation and discriminative dictionary learning (DDL) algorithm has become a widely-used model in visual recognition systems, and various discrimination terms are introduced into the DDL models to enhance the discriminative ability and the recognition rate. Recently, an algorithm named dictionary pair learning (DPL) was proposed which jointly learned a synthesis dictionary and an analysis dictionary to promote the recognition performance. In this paper, a novel dictionary learning model is proposed which introduces a differentiable support vector discriminative term into the original DPL model. In the dictionary learning stage, the proposed model can jointly train a synthesis dictionary, an analysis dictionary and a support vector discriminative term. In the classification stage, the class label is decided by the joint effect of the reconstruction residual, the projective discrimination term and the support vector function. Experimental results on various image recognition benchmarks such as face recognition, scene categorization and object classification are presented to demonstrate the effectiveness of the proposed method.

Multi-feature kernel discriminant dictionary learning for face recognition

The current study put forward a multi-feature kernel discriminant dictionary learning algorithm for face recognition. It was based on the supervised within-class-similar discriminative dictionary learning algorithm (SCDDL) we introduced previously. The proposed new algorithm was thus named as multi-feature kernel SCDDL (MKSCDDL). In contrast to the weighted combination or the constraint of representation coefficients for the feature combination used by some popular methods, MKSCDDL introduced the multiple kernel learning technique into the dictionary learning scheme. The experimental results on three large well-known face databases suggested that combination multiple features in MKSCDDL improved the recognition rate compared with SCDDL. In addition, adopting multiple kernel learning technique resulted in an excellent multi-feature dictionary learning approach when compared with some state-of-the-art multi-feature algorithms such as multiple kernel learning and multi-task joint sparse representation methods, indicating the effectiveness of the multiple kernel learning technique in the combination of multiple features for classification.

A test sample oriented two-phase discriminative dictionary learning algorithm for face recognition

A test sample oriented two-phase discriminative dictionary learning algorithm for face recognition

Tied factor analysis for unconstrained face pose classification

Visual classification of facial pose is desirable for computer vision applications such as face recognition, human computer interaction, and affective computing. However, accurate classification of facial pose in an unconstrained environment presents a great challenge. Extant conventional approaches lack the capability to deal with multiple pose-related and -unrelated factors in a uniform way. This paper proposes a robust pose classification framework based on dictionary-learning and sparse representation. With the guide of this framework, a novel face image pre-processing algorithm based on Gaussian weighted and tip of nose is designed to enhance pose-related factors. Further, a new discriminative dictionary learning algorithm is designed for learning a dictionary from training samples to enhance the discriminative capability of its coding vectors. We provide a new insight on discriminative dictionary learning. Specifically, we formulate the discrimination term based on factors analysis. In order to improve the robustness against face occlusion, we introduce a pose occlusion dictionary to code the occluded face images. Several experiments are performed on XJTU，CMU Multi-PIE CAS-PEAL-R1, GTAV and AFLW databases. Recognition results show that the proposed method can achieve recognition rate about 95% under illumination, noises and occlusion variations and thus, is eminently practical.

Toward semantic attributes in dictionary learning and non-negative matrix factorization

Binary label information is widely used semantic information in discriminative dictionary learning and non-negative matrix factorization. A Discriminative Dictionary Learning (DDL) algorithm uses the label of some data samples to enhance the discriminative property of sparse signals. A discriminative Non-negative Matrix Factorization (NMF) utilizes label information in learning discriminative bases. All these technique are using binary label information as semantic information. In contrast to such binary attributes or labels, relative attributes contain richer semantic information where the data is annotated with the strength of the attributes. In this paper, we utilize the relative attributes of training data in non-negative matrix factorization and dictionary learning. Precisely, we learn rank functions (one for each predefined attribute) to rank the images based on predefined semantic attributes. The strength of each attribute in a data sample is used as semantic information. To assess the quality of the obtained signals, we apply k-means clustering and measure the performance for clustering. Experimental results conducted on three datasets, namely PubFig (16), OSR (24) and Shoes (15) confirm that the proposed approach outperforms the state-of-the-art discriminative algorithms.

Supervised within-class-similar discriminative dictionary learning for face recognition

The current study puts forward a supervised within-class-similar discriminative dictionary learning (SCDDL) algorithm for face recognition. Some popular discriminative dictionary learning schemes for recognition tasks always incorporate the linear classification error term into the objective function or make some discriminative restrictions on representation coefficients. In the presented SCDDL algorithm, we propose to directly restrict the representation coefficients to be similar within the same class and simultaneously include the linear classification error term in the supervised dictionary learning scheme to derive a more discriminative dictionary for face recognition. The experimental results on three large well-known face databases suggest that our approach can enhance the fisher ratio of representation coefficients when compared with several dictionary learning algorithms that incorporate linear classifiers. In addition, the learned discriminative dictionary, the large fisher ratio of representation coefficients and the simultaneously learned classifier can improve the recognition rate compared with some state-of-the-art dictionary learning algorithms.

Discriminative structured dictionary learning for image classification

In this paper, a discriminative structured dictionary learning algorithm is presented. To enhance the dictionary’s discriminative power, the reconstruction error, classification error and inhomogeneous representation error are integrated into the objective function. The proposed approach learns a single structured dictionary and a linear classifier jointly. The learned dictionary encourages the samples from the same class to have similar sparse codes, and the samples from different classes to have dissimilar sparse codes. The solution to the objective function is achieved by employing a feature-sign search algorithm and Lagrange dual method. Experimental results on three public databases demonstrate that the proposed approach outperforms several recently proposed dictionary learning techniques for classification.

A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification.

Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.

Single-channel speech separation using sequential discriminative dictionary learning

A novel sequential discriminative dictionary learning (SDDL) algorithm is presented to suppress the confusion between the separated signals which we denote source confusion. The existing discriminative dictionary learning (DDL) algorithms assume that signals from different speakers have their unique components which makes that the signals can be explained by the corresponding sub-dictionaries. But the signals from different speakers have similar components when divided into speech segments. We take the unique and similar components of different speakers׳ signals into account, and design a new structured dictionary which contains discriminative and buffer sub-dictionaries. The unique components of different speakers׳ signals which have better correspondences to the speakers׳ labels are firstly separated, and the similar components of different speakers׳ signals are separated in the next layer. An objective function is derived, which guarantees that the unique components of the training sets can be explained by their corresponding discriminative sub-dictionaries and the similar components of the training sets can be explained by the buffer sub-dictionary rather than the cross sub-dictionary. The components distributed in the buffer sub-dictionary are used as training sets in the next layer. Experiments results verify that the proposed algorithm can effectively reduce the source confusion compared to the existing algorithms.

Dictionaries for image and video-based face recognition [Invited

In recent years, sparse representation and dictionary-learning-based methods have emerged as powerful tools for efficiently processing data in nontraditional ways. A particular area of promise for these theories is face recognition. In this paper, we review the role of sparse representation and dictionary learning for efficient face identification and verification. Recent face recognition algorithms from still images, videos, and ambiguously labeled imagery are reviewed. In particular, discriminative dictionary learning algorithms as well as methods based on weakly supervised learning and domain adaptation are summarized. Some of the compelling challenges and issues that confront research in face recognition using sparse representations and dictionary learning are outlined.

HEp-2 cell pattern classification with discriminative dictionary learning

The paper presents a supervised discriminative dictionary learning algorithm specially designed for classifying HEp-2 cell patterns. The proposed algorithm is an extension of the popular K-SVD algorithm: at the training phase, it takes into account the discriminative power of the dictionary atoms and reduces their intra-class reconstruction error during each update. Meanwhile, their inter-class reconstruction effect is also considered. Compared to the existing extension of K-SVD, the proposed algorithm is more robust to parameters and has better discriminative power for classifying HEp-2 cell patterns. Quantitative evaluation shows that the proposed algorithm outperforms general object classification algorithms significantly on standard HEp-2 cell patterns classifying benchmark11An early version of this algorithm ranks 2nd out of 28 algorithms in the 1st international contest on HEp-2 Cells classification [1]. and also achieves competitive performance on standard natural image classification benchmark.

Human Action Recognition Based on Discriminative Sparse Coding Video Representation

The bag-of-words(BoW) model usually causes large errors and weak discrimination in video representation in video action recognition,and affects the human action recognition accuracy.To solve this problem,a discriminative sparse coding(DSC) video representation algorithm is proposed.It’s a sparse coding framework involving a Fisher discriminative analysis to encode video local spatial-temporal features and increase the video sparse representation discrimination.And an online discriminative dictionary learning algorithm is also proposed to train a dictionary from massive video data.The experiments show that,comparing with the existing algorithms,the proposed algorithm effectively improves human action recognition accuracy.