Andy Jinhua Ma

Multi-cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation

The use of multiple features for tracking has been proved as an effective approach because limitation of each feature could be compensated. Since different types of variations such as illumination, occlusion and pose may happen in a video sequence, especially long sequence videos, how to dynamically select the appropriate features is one of the key problems in this approach. To address this issue in multi-cue visual tracking, this paper proposes a new joint sparse representation model for robust feature-level fusion. The proposed method dynamically removes unreliable features to be fused for tracking by using the advantages of sparse representation. As a result, robust tracking performance is obtained. Experimental results on publicly available videos show that the proposed method outperforms both existing sparse representation based and fusion-based trackers.

Joint Sparse Representation and Robust Feature-Level Fusion for Multi-Cue Visual Tracking

Visual tracking using multiple features has been proved as a robust approach because features could complement each other. Since different types of variations such as illumination, occlusion, and pose may occur in a video sequence, especially long sequence videos, how to properly select and fuse appropriate features has become one of the key problems in this approach. To address this issue, this paper proposes a new joint sparse representation model for robust feature-level fusion. The proposed method dynamically removes unreliable features to be fused for tracking by using the advantages of sparse representation. In order to capture the non-linear similarity of features, we extend the proposed method into a general kernelized framework, which is able to perform feature fusion on various kernel spaces. As a result, robust tracking performance is obtained. Both the qualitative and quantitative experimental results on publicly available videos show that the proposed method outperforms both sparse representation-based and fusion based-trackers.

Domain Transfer Support Vector Ranking for Person Re-identification without Target Camera Label Information

This paper addresses a new person re-identification problem without the label information of persons under non-overlapping target cameras. Given the matched (positive) and unmatched (negative) image pairs from source domain cameras, as well as unmatched (negative) image pairs which can be easily generated from target domain cameras, we propose a Domain Transfer Ranked Support Vector Machines (DTRSVM) method for re-identification under target domain cameras. To overcome the problems introduced due to the absence of matched (positive) image pairs in target domain, we relax the discriminative constraint to a necessary condition only relying on the positive mean in target domain. By estimating the target positive mean using source and target domain data, a new discriminative model with high confidence in target positive mean and low confidence in target negative image pairs is developed. Since the necessary condition may not truly preserve the discriminability, multi-task support vector ranking is proposed to incorporate the training data from source domain with label information. Experimental results show that the proposed DTRSVM outperforms existing methods without using label information in target cameras. And the top 30 rank accuracy can be improved by the proposed method upto 9.40% on publicly available person re-identification datasets.

Cross-Domain Person Reidentification Using Domain Adaptation Ranking SVMs

This paper addresses a new person reidentification problem without label information of persons under nonoverlapping target cameras. Given the matched (positive) and unmatched (negative) image pairs from source domain cameras, as well as unmatched (negative) and unlabeled image pairs from target domain cameras, we propose an adaptive ranking support vector machines (AdaRSVMs) method for reidentification under target domain cameras without person labels. To overcome the problems introduced due to the absence of matched (positive) image pairs in the target domain, we relax the discriminative constraint to a necessary condition only relying on the positive mean in the target domain. To estimate the target positive mean, we make use of all the available data from source and target domains as well as constraints in person reidentification. Inspired by adaptive learning methods, a new discriminative model with high confidence in target positive mean and low confidence in target negative image pairs is developed by refining the distance model learnt from the source domain. Experimental results show that the proposed AdaRSVM outperforms existing supervised or unsupervised, learning or non-learning reidentification methods without using label information in target cameras. Moreover, our method achieves better reidentification performance than existing domain adaptation methods derived under equal conditional probability assumption.

Linear Dependency Modeling for Classifier Fusion and Feature Combination

This paper addresses the independent assumption issue in fusion process. In the last decade, dependency modeling techniques were developed under a specific distribution of classifiers or by estimating the joint distribution of the posteriors. This paper proposes a new framework to model the dependency between features without any assumption on feature/classifier distribution, and overcomes the difficulty in estimating the high-dimensional joint density. In this paper, we prove that feature dependency can be modeled by a linear combination of the posterior probabilities under some mild assumptions. Based on the linear combination property, two methods, namely, Linear Classifier Dependency Modeling (LCDM) and Linear Feature Dependency Modeling (LFDM), are derived and developed for dependency modeling in classifier level and feature level, respectively. The optimal models for LCDM and LFDM are learned by maximizing the margin between the genuine and imposter posterior probabilities. Both synthetic data and real datasets are used for experiments. Experimental results show that LCDM and LFDM with dependency modeling outperform existing classifier level and feature level combination methods under nonnormal distributions and on four real databases, respectively. Comparing the classifier level and feature level fusion methods, LFDM gives the best performance.

Reduced Analytic Dependency Modeling: Robust Fusion for Visual Recognition

This paper addresses the robustness issue of information fusion for visual recognition. Analyzing limitations in existing fusion methods, we discover two key factors affecting the performance and robustness of a fusion model under different data distributions, namely (1) data dependency and (2) fusion assumption on posterior distribution. Considering these two factors, we develop a new framework to model dependency based on probabilistic properties of posteriors without any assumption on the data distribution. Making use of the range characteristics of posteriors, the fusion model is formulated as an analytic function multiplied by a constant with respect to the class label. With the analytic fusion model, we give an equivalent condition to the independent assumption and derive the dependency model from the marginal distribution property. Since the number of terms in the dependency model increases exponentially, the Reduced Analytic Dependency Model (RADM) is proposed based on the convergent property of analytic function. Finally, the optimal coefficients in the RADM are learned by incorporating label information from training data to minimize the empirical classification error under regularized least square criterion, which ensures the discriminative power. Experimental results from robust non-parametric statistical tests show that the proposed RADM method statistically significantly outperforms eight state-of-the-art score-level fusion methods on eight image/video datasets for different tasks of digit, flower, face, human action, object, and consumer video recognition.

Supervised Spatio-Temporal Neighborhood Topology Learning for Action Recognition

Supervised manifold learning has been successfully applied to action recognition, in which class label information could improve the recognition performance. However, the learned manifold may not be able to well preserve both the local structure and global constraint of temporal labels in action sequences. To overcome this problem, this paper proposes a new supervised manifold learning algorithm called supervised spatio-temporal neighborhood topology learning (SSTNTL) for action recognition. By analyzing the topological characteristics in the context of action recognition, we propose to construct the neighborhood topology using both supervised spatial and temporal pose correspondence information. Employing the property in locality preserving projection (LPP), SSTNTL solves the generalized eigenvalue problem to obtain the best projections that not only separates data points from different classes, but also preserves local structures and temporal pose correspondence of sequences from the same class. Experimental results demonstrate that SSTNTL outperforms the manifold embedding methods with other topologies or local discriminant information. Moreover, compared with state-of-the-art action recognition algorithms, SSTNTL gives convincing performance for both human and gesture action recognition.

Linear dependency modeling for feature fusion

This paper addresses the independent assumption issue in fusion process. In the last decade, dependency modeling techniques were developed under a specific distribution of classifiers. This paper proposes a new framework to model the dependency between features without any assumption on feature/classifier distribution. In this paper, we prove that feature dependency can be modeled by a linear combination of the posterior probabilities under some mild assumptions. Based on the linear combination property, two methods, namely Linear Classifier Dependency Modeling (LCDM) and Linear Feature Dependency Modeling (LFDM), are derived and developed for dependency modeling in classifier level and feature level, respectively. The optimal models for LCDM and LFDM are learned by maximizing the margin between the genuine and imposter posterior probabilities. Both synthetic data and real datasets are used for experiments. Experimental results show that LFDM outperforms all existing combination methods.

Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population

Non‐alcoholic fatty liver disease (NAFLD) affects 20%‐40% of the general population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Electronic medical records facilitate large‐scale epidemiological studies, existing NAFLD scores often require clinical and anthropometric parameters that may not be captured in those databases.

Query Based Adaptive Re-ranking for Person Re-identification

Existing algorithms for person re-identification hardly model query variations across non-overlapping cameras. In this paper, we propose a query based adaptive re-ranking method to address this important issue. In our work, negative image pairs can be easily generated for each query under non-overlapping cameras. To infer query variations across cameras, nearest neighbors of the query positive match under two camera views are approximated and selected from positive matches in the training set. Locality preserving projections (LPP) are employed to ensure that each feature vector under one camera shares similar neighborhood structure with the corresponding positive match. Using existing re-identification algorithms as base score function, the optimal adaptive model is learnt by least-square regression with manifold regularization. Experimental results show that the proposed method can improve the ranking performance and outperforms other adaptive methods.