Liangzheng Xia

A hybrid classifier for precise and robust eye detection

Eye location is an important visual cue for face image processing such as alignment before face recognition, gaze tracking, expression analysis, etc. In this paper a novel eye detection algorithm is presented, which integrates the characteristics of single eye and eye-pair images to develop a hybrid classifier under the learning paradigm. The low dimensional features representing eye patterns yield by subspace projection are selected via a filter and a wrapper method for a simplified maximum likelihood and a SVM classifier respectively. Eye candidates determined by a cascade of the two classifiers are further verified with eye-pair template matching scores to reject false detections. The performance of this eye detector is assessed on several publicly available face databases and the experimental results demonstrate its robustness to the variations in head pose, facial expressions, partial occlusions and lighting conditions

Efficient and accurate face detection using heterogeneous feature descriptors and feature selection

The performance of an efficient and accurate face detection system depends on several issues: (1) distinctive representation for face patterns; (2) effective algorithm for feature selection and classifier learning; (3) suitable framework for rapid background removal. To address the first issue, we propose to represent face patterns with a set of heterogeneous and complementary feature descriptors including the Generalized Haar-like (GH) descriptor, Multi-Block Local Binary Patterns (MB-LBP) descriptor and Speeded-Up Robust Features (SURF) descriptor. To address the second issue, Particle Swarm Optimization (PSO) algorithm is incorporated into the Adaboost framework, replacing the exhaustive search used in original Adaboost for efficient feature selection. The utilization of heterogeneous feature descriptors enriches the diversity of feature types for Adaboost learning algorithm. As a result, classification performance of the boosted ensemble classifier also improves significantly. A three-stage hierarchical classifier structure is proposed to tackle the last issue. In particular, a new stage is added to detect candidate face regions more quickly by using a large size window with a large moving step. Nonlinear support vector machine (SVM) classifiers are used instead of decision stump classifiers in the last stage to remove those remaining complex non-face patterns that cannot be rejected in the previous two stages. Combining the abovementioned effective modules, we derive the proposed Hetero-PSO-Adaboost-SVM face detector that achieves superior detection accuracy while maintaining a low training and detection complexity. Extensive experiments demonstrate the robustness and efficiency of our system by comparing it with several popular state-of-the-art algorithms on our own test set as well as the widely used CMU+MIT frontal and CMU profile face dataset.

Tree-Structured Support Vector Machines for Multi-class Classification

In this paper, a non-balanced binary tree is proposed for extending support vector machines (SVM) to multi-class problems. The non-balanced binary tree is constructed based on the prior distribution of samples, which can make the more separable classes separated at the upper node of the binary tree. For an kclass problem, this method only needs k-1 SVM classifiers in the training phase, while it has less than kbinary test when making a decision. Further, this method can avoid the unclassifiable regions that exist in the conventional SVMs. The experimental result indicates that maintaining comparable accuracy, this method is faster than other methods in classification.

Context-based embedded image compression using binary wavelet transform

Binary wavelet transform (BWT) has several distinct advantages over the real wavelet transform (RWT), such as the conservation of alphabet size of wavelet coefficients, no quantization introduced during the transform and the simple Boolean operations involved. Thus, less coding passes are engaged and no sign bits are required in the compression of transformed coefficients. However, the use of BWT for the embedded grayscale image compression is not well established. This paper proposes a novel Context-based Binary Wavelet Transform Coding approach (CBWTC) that combines the BWT with a high-order context-based arithmetic coding scheme to embedded compression of grayscale images. In our CBWTC algorithm, BWT is applied to decorrelate the linear correlations among image coefficients without expansion of the alphabet size of symbols. In order to match up with the CBWTC algorithm, we employ the gray code representation (GCR) to remove the statistical dependencies among bi-level bitplane images and develop a combined arithmetic coding scheme. In the proposed combined arithmetic coding scheme, three highpass BWT coefficients at the same location are combined to form an octave symbol and then encoded with a ternary arithmetic coder. In this way, the compression performance of our CBWTC algorithm is improved in that it not only alleviate the degradation of predictability caused by the BWT, but also eliminate the correlation of BWT coefficients in the same level subbands. The conditional context of the CBWTC is properly modeled by exploiting the characteristics of the BWT as well as taking advantages of non-causal adaptive context modeling. Experimental results show that the average coding performance of the CBWTC is superior to that of the state-of-the-art grayscale image coders, and always outperforms the JBIG2 algorithm and other BWT-based binary coding technique for a set of test images with different characteristics and resolutions.

Fusing multi-feature representation and PSO-Adaboost based feature selection for reliable frontal face detection

We propose a reliable frontal face detector based on multifeature descriptors and feature selection using PSO-Adaboost. Utilization of multiple heterogeneous feature descriptors enriches the diversity of feature types for face modeling and feature learning. To speed up the training process of face detector, we also propose a PSO-Adaboost algorithm that replaces exhaustive search used in original Adaboost framework with Particle Swarm Optimization (PSO) technique for efficient feature selection. Finally, a three-stage cascade classifier is developed to remove background rapidly. In particular, an initial stage is designed to detect candidate face regions more quickly by using a large size window with a large moving step. Radial Basis Function (RBF) SVM classifiers are used instead of decision stump functions in the last stage to remove those remaining complex non-face patterns that can not be rejected in the previous two stages. Combining these three effective modules, our face detector achieves a detection rate of 96.50% at ten false positives on the CMU+MIT frontal face dataset.

Mining heterogeneous class-specific codebook for categorical object detection and classification

We propose a novel model to mine and derive class-specific codebook for categorical object detection and classification. In particular, the codebook is built from a pool of heterogeneous local descriptors using an effective feature selection scheme. The resulting class-specific codebook strengthens the class discriminability by learning the most discriminative part codewords constructed from their preferable local descriptors. The advantage of our class-specific codebook comes from two aspects. 1). As we collect a variety of heterogeneous descriptors during the learning of local codebook, each target object class can always be represented by its most preferable descriptors. Moreover, even each part codeword can also find its suitable descriptors. 2). The feature selection process further picks out the most discriminative object parts that separate the target object class from background and other classes. Experimental results on several widely used datasets show that benefits from our class-specific object codebook which fuses complementary visual cues remarkably improve the detection and classification performance for both rigid and non-rigid articulated objects.

Ship detection algorithm used in a navigation lock monitored system

A ship detection algorithm used in a navigation lock monitored control system, which helps to improve safety and dependability, is presented. We mean, in this paper, the processed region of river channel is determined through image edge detection. A method of peak-cutting histogram is introduced to restrain illusive movement information resulting from the glisten of water in the navigation lock. A concept of difference compactness from computing the projected density function of difference image is put forward, which helps to detect moving ships more precisely. With the statistical property of histogram of several small regions of river channel: gray scale variance, the detection of stationary ships can be accomplished, and the results can be judged by confidence region. Computer simulation has proved that it contributes a lot to the detection of ships in a navigation lock.

Efficient face recognition fusing dynamic morphological quotient image with local binary pattern

In this paper, we propose a novel illumination normalized Local Binary Pattern (LBP)-based algorithm for face recognition under varying illumination conditions. The proposed DMQI-LBP algorithm fuses illumination normalization, using the Dynamic Morphological Quotient Image (DMQI), into the current LBP-based face recognition system. So it makes full use of advantages of illumination compensation offered by the quotient image, estimated with a dynamic morphological close operation, as well as the powerful discrimination ability provided by the LBP descriptor. Evaluation results on the Yale face database B indicate that the proposed DMQI-LBP algorithm significantly improve the recognition performance (by 5% for the first rank) of the original raw LBP-based system for face recognition with severe lighting variations. Furthermore, our algorithm is efficient and simple to implement, which makes it very suitable for real-time face recognition.

Combining generic and class-specific codebooks for object categorization and detection

Combining advantages of shape and appearance features, we propose a novel model that integrates these two complementary features into a common framework for object categorization and detection. In particular, generic shape features are applied as a pre-filter that produces initial detection hypotheses following a weak spatial model, then the learnt class-specific discriminative appearance-based SVM classifier using local kernels verifies these hypotheses with a stronger spatial model and filter out false positives. We also enhance the discriminability of appearance codebooks for the target object class by selecting several most discriminative part codebooks that are built upon a pool of heterogeneous local descriptors, using a classification likelihood criterion. Experimental results show that both improvements significantly reduce the number of false positives and cross-class confusions and perform better than methods using only one cue.

A binary wavelet-based scheme for grayscale image compression

This paper proposes a novel grayscale image compression approach using the binary wavelet transform (BWT) and context-based arithmetic coding, namely the context-based binary wavelet transform coding algorithm (CBWTC). In our CBWTC, in order to alleviate the degradation of predictability caused by the BWT and eliminate the correlation within the same level subbands, three highpass wavelet coefficients at the same location are combined to form an octave symbol and then encoded with a ternary arithmetic coder. The conditional context of the CBWTC is properly modeled by exploiting the properties of the BWT as well as taking the advantages of non-causal adaptive context modeling. Experimental results show that the coding performance of the CBWTC is better than that of the state-of-the-art grayscale image coders except for images containing rich texture, and always outperforms the JBIG2 algorithm and other BWT-based binary coding technique.