Bang Zhang

Mutual information-based method for selecting informative feature sets

Feature selection is one of the fundamental problems in pattern recognition and data mining. A popular and effective approach to feature selection is based on information theory, namely the mutual information of features and class variable. In this paper we compare eight different mutual information-based feature selection methods. Based on the analysis of the comparison results, we propose a new mutual information-based feature selection method. By taking into account both the class-dependent and class-independent correlation among features, the proposed method selects a less redundant and more informative set of features. The advantage of the proposed method over other methods is demonstrated by the results of experiments on UCI datasets (Asuncion and Newman, 2010 [1]) and object recognition.

Finding shareable informative patterns and optimal coding matrix for multiclass boosting

A multiclass classification problem can be reduced to a collection of binary problems using an error-correcting coding matrix that specifies the binary partitions of the classes. The final classifier is an ensemble of base classifiers learned on binary problems and its performance is affected by two major factors: the qualities of the base classifiers and the coding matrix. Previous studies either focus on one of these factors or consider two factors separately. In this paper, we propose a new multiclass boosting algorithm called AdaBoost.SIP that considers both two factors simultaneously. In this algorithm, informative patterns, which are shareable by different classes rather than only discriminative on specific single class, are generated at first. Then the binary partition preferred by each pattern is found by performing stage-wise functional gradient descent on a margin-based cost function. Finally, base classifiers and coding matrix are optimized simultaneously by maximizing the negative gradient of such cost function. The proposed algorithm is applied to scene and event recognition and experimental results show its effectiveness in multiclass classification.

Region-based image categorization with reduced feature set

In this paper we propose a new algorithm for region-based image categorization that is formulated as a multiple instance learning (MIL) problem. The proposed algorithm transforms the MIL problem into a traditional supervised learning problem, and solves it using a standard supervised learning method. The features used in the proposed algorithm are the hyperclique patterns which are ldquocondensedrdquo into a small set of discriminative features. Each hyperclique pattern consists of multiple strongly-correlated instances (i.e., features). As a result, hyperclique patterns are able to capture the information that are not shared by individual features. The advantages of the proposed algorithm over existing algorithms are threefold: (i) unlike some existing algorithms which use learning methods that are specifically designed for MIL or for certain datasets, the proposed algorithm uses a general-purpose standard supervised learning method, (ii) it uses a significantly small set of features which are empirically more discriminative than the PCA features (i.e. principal components), and (iii) it is simple and efficient and achieves a comparable performance to most state-of-the-art algorithms. The efficiency and good performance of the proposed algorithm make it a practical solution to general MIL problems. In this paper, we apply the proposed algorithm to both drug activity prediction and image categorization, and promising results are obtained.

Feature clustering for vehicle detection and tracking in road traffic surveillance

In this paper, we formulate the feature clustering problem for vehicle detection and tracking as a general MAP problem and solve it using MCMC. The proposed approach exhibits two advantages over existing methods: general Bayesian model can handle arbitrary objective functions and MCMC guarantees global optimal solution. Our algorithm is validated on real-world traffic video sequences, and is shown to outperform the state-of-the-art approach.

Batch mode active learning for multi-label image classification with informative label correlation mining

The performances of supervised learning techniques on image classification problems heavily rely on the quality of their training images. But the acquisition of high quality training images requires significant efforts from human annotators. In this paper, we propose a novel multi-label batch model active learning (MLBAL) approach that allows the learning algorithm to actively select a batch of informative example-label pairs from which it learns at each learning iteration, so as to learn accurate classifiers with less annotation efforts. Unlike existing methods, the proposed approach fines the active selection granularity from example to example-label pair, and takes into account the informative label correlations for active learning. And the empirical studies demonstrate its effectiveness.

Incremental EM for Probabilistic Latent Semantic Analysis on Human Action Recognition

Human action recognition is a significant task in automatic understanding systems for video surveillance. Probabilistic Latent Semantic Analysis (PLSA) model has been used to learn and recognize human actions in videos. Specifically, PLSA employs the expectation maximization (EM) algorithm for parameter estimation during the training. The EM algorithm is an iterative estimation scheme that is guaranteed to find a local maximum of the likelihood function. However its convergence usually takes a large number of iterations. For action recognition with large amount of training data, this would result in long training time. This paper presents an incremental version of EM to speed up the training of PLSA without sacrificing performance accuracy. The proposed algorithm is tested on two challenging human action datasets. Experimental results demonstrate that the proposed algorithm converges with fewer number of full passes compared with the batch EM algorithm. And the trained PLSA models achieve comparable or better recognition accuracies than those using batch EM training.

A practical approach to multiple super-resolution sprite generation

The MPEG-4 video coding standard introduces a novel concept of sprite or mosaic that is a large image composed of pixels belonging to a video object visible throughout a video segment. The sprite captures spatio-temporal information in a very compact way and makes it possible for efficient object-based video compression. In this paper, we propose a practical approach to generating multiple super-resolution sprites for sprite coding. In order to construct super-resolution sprites and reduce coding cost, we firstly partition a video sequence into multiple independent sprites and group the images covering a similar scene into the same sprite. We then propose efficient and practical algorithms for cumulative global motion estimation and super-resolution sprite construction. Experiments with real video sequences show that the proposed approach outperforms the previous single sprite and multiple sprite techniques.

Multi-class Graph Boosting with Subgraph Sharing for Object Recognition

In this paper, we propose a novel multi-class graph boosting algorithm to recognize different visual objects. The proposed method treats subgraph as feature to construct base classifier, and utilizes popular error correcting output code scheme to solve multi-class problem. Both factors, base classifier and error-correcting coding matrix are considered simultaneously. And subgragphs, which are shareable by different classes, are wisely used to improve the classification performance. The experimental results on multi-class object recognition show the effectiveness of the proposed algorithm.

Feature fusion for vehicle detection and tracking with low-angle cameras

Vision-based vehicle detection is a critical task for traffic monitoring in modern Intelligent Traffic Systems (ITS). Due to the low-angle nature of most traffic surveillance cameras installed in the real world, vehicle detection in such case has to deal with one fundamental challenge — occlusion, which renders most traditional vehicle detection methods ineffective. In this paper, instead of detecting the vehicle as a whole, we propose a vehicle detection algorithm based on windshield model matching. By detecting windshield directly, the algorithm achieves robustness to occlusion. Together with camera calibration and vehicle tracking, the system is able to provide reliable traffic state estimation. Experiments on real traffic videos demonstrate the better performance of our system compared to the state-of-the-art algorithm.

An efficient approach to detecting pedestrians in video

In this paper, we propose an efficient approach to moving pedestrian detection in video. This approach incorporates both motion and shape information and learns a codebook of shape context descriptors from a very small number of training samples. During the testing process, moving edgelets are firstly identified between adjacent frames using a local search method. Shape context descriptors for numerous sample points on identified edgelets are then produced and are matched against the instances of the learned codebook to generate initial hypotheses. The final hypotheses for pedestrians are obtained by pruning initial hypotheses. The proposed approach has the following advantages by comparison with the existing techniques: (1) lower computational cost, (2) lower false positive rate, and (3) fewer training samples. Experiments with a publicly available dataset confirm the performance of the proposed approach.