Liujuan Cao

A novel features ranking metric with application to scalable visual and bioinformatics data classification

Coming with the big data era, the filtering of uninformative data becomes emerging. To this end, ranking the high dimensionality features plays an important role. However, most of the state-of-art methods focus on improving the classification accuracy while the stability of the dimensionality reduction is simply ignored. In this paper, we proposed a Max-Relevance-Max-Distance (MRMD) feature ranking method, which balances accuracy and stability of feature ranking and prediction task. In order to prove the effectiveness on big data, we tested our method on two different datasets. The first one is image classification, which is a benchmark dataset with high dimensionality, while the second one is proteinprotein interaction prediction data, which comes from our previous private research and has massive instances. Experiments prove that our method maintained the accuracy together with the stability on both two big datasets. Moreover, our method runs faster than other filtering and wrapping methods, such as mRMR and Information Gain.

Vehicle Detection in High-Resolution Aerial Images via Sparse Representation and Superpixels

This paper presents a study of vehicle detection from high-resolution aerial images. In this paper, a superpixel segmentation method designed for aerial images is proposed to control the segmentation with a low breakage rate. To make the training and detection more efficient, we extract meaningful patches based on the centers of the segmented superpixels. After the segmentation, through a training sample selection iteration strategy that is based on the sparse representation, we obtain a complete and small training subset from the original entire training set. With the selected training subset, we obtain a dictionary with high discrimination ability for vehicle detection. During training and detection, the grids of histogram of oriented gradient descriptor are used for feature extraction. To further improve the training and detection efficiency, a method is proposed for the defined main direction estimation of each patch. By rotating each patch to its main direction, we give the patches consistent directions. Comprehensive analyses and comparisons on two data sets illustrate the satisfactory performance of the proposed algorithm.

Single/cross-camera multiple-person tracking by graph matching

Single and cross-camera multiple person tracking in unconstrained condition is an extremely challenging task in computer vision. Facing the main difficulties caused by the existence of occlusion in single-camera scenario and the occurrence of transition in cross-camera scenario, we propose a unified framework formulated in graph matching with affinity constraints for both single and cross-camera tracking tasks. To our knowledge, our work is the first to unify two kinds of tracking problems with the same framework by graph matching. The proposed method consists of two steps, tracklet generation and tracklet association. First, we implement the modified part-based human detector and the Tracking-Modeling-Detection (TMD) method for tracklet generation. Then we propose to associate tracklets by graph matching which is mathematically formulated into the Rayleigh Quotients Maximization. The comparison experiments show that the proposed method can produce the competing results with the state-of-the-art methods.

Nonlinear scrambling-based reversible watermarking for 2D-vector maps

The reversible watermarking technique is suitable for vector maps due to its reversibility after watermark extraction. In this paper, a novel reversible watermarking scheme based on the idea of nonlinear scrambling is proposed. It begins with feature point extraction. To avoid the high-precision vector data being illegally used by unauthorized users, the algorithm nonlinearly scrambles the relative position of feature points. Then based on the proposed reversible embedding, both scrambled feature points and nonfeature points are taken as cover data, the coordinates of which are modified to embed both watermark data and feature point identification data. Finally, combined with the scrambling secret key, the original vector data can be exactly recovered with watermark extraction. Comprehensive experimental results validate that the scheme could effectively prevent the high-precision vector data from being illegally used with maintaining the basic shape of each polyline, simultaneously.

A recursive embedding algorithm towards lossless 2D vector map watermarking

The copyright protection of two-dimensional (2D) vector map has attracted a lot of research focus due to the increasing security issues raised in recent years. One promising direction seeking the optimal tradeoff between adding watermarks and maintaining minimal distortion is the so-called lossless watermarking, i.e., after watermark extraction the 2D vector maps are fully lossless. This paper presents a novel lossless watermarking scheme for 2D vector maps based on a novel recursive embedding algorithm. In our algorithm, feature points of individual polylines are first grouped into united, upon which highly correlated unites are selected as cover data to carry out a recursive modification of its mean vertex coordinates. Such operation not only ensures lossless compression, but also enables higher payload capacity and, to a certain degree, the perception invisibility before and after the watermark extraction. We have conduced experiments on several real-world 2D vector map applications to show the effectiveness, efficiency of the proposed algorithm.

High-capacity reversible watermarking scheme of 2D-vector data

The application of two-dimensional (2D) vector map faces the security issues of copyright protection, which limit the usage of vector data in many scenarios. Reversible watermarking is a more feasible solution, which aims to restore the original data after watermark extraction. In this paper, high-capacity reversible watermarking scheme of 2D-vector data is proposed based on the idea of iterative embedding. It groups vertices as units for each polyline and selects highly correlated vertex units as cover data. Then the reversible embedding is carried out by iteratively modifying the median vertex coordinates of each selected embedding unit. This scheme can strictly recover the original vector data with watermark accurate extraction. Meanwhile, both higher payload capacity and perception invisibility are validated through theoretical analysis and comprehensive experiments. The experimental results show that the proposed reversible watermarking scheme is suitable for 2D-vector data applications where high-precision data are required.

Robust depth-based object tracking from a moving binocular camera

Depth is a rich source of information and has been successfully utilized in numerous computer vision applications. However, it is often ignored in object tracking. In this paper, in contrast to traditional 2D image-based tracking method, we propose a novel 3D object tracking method from a moving binocular camera. To effectively handle the deformable targets, a target is first represented by a local patch-based appearance model. Then, to handle the partial occlusions, we design a simple yet effective scheme to detect and recovery occlusions using depth information obtained from a moving binocular camera. Therefore, the proposed method can simultaneous capture target appearance changes and alleviate the drifting problem. The experimental results demonstrate the effectiveness of the proposed method. A Robust Depth-based Object Tracking Algorithm from a Moving Binocular Camera.The key idea is firstly to utilize a local patch-based appearance model to represent the target. Since the target is represented by a l local patch-based appearance model, the proposed tracking method can effectively handle the deformable targets. Then, to handle the partial occlusions, we use a simple yet effective scheme to detect and recovery occlusions using depth information obtained from a moving binocular camera, which is more robust than existing 2D image-based tracking methods. Experimental results on challenging video sequences demonstrate the robustness of the proposed 3D tracking method by comparing it with several state-of-the-art tracking methods.

News videos anchor person detection by shot clustering

In recent years, extensive research efforts have been dedicated to automatic news content analysis. In this paper, we propose a novel algorithm for anchorperson detection in news video sequences. In this method, the raw news videos are firstly split into shots by a four-threshold method, and the key frames are extracted from each shot. After that, the anchorperson detection is conducted from these key frames by using a clustering-based method based on a statistical distance of Pearsons correlation coefficient. To evaluate the effectiveness of the proposed method, we have conducted experiments on 10 news sequences. In these experiments, the proposed scheme achieves a recall of 0.96 and a precision of 0.97 for anchorperson detection.

Vehicle Detection in High-Resolution Aerial Images Based on Fast Sparse Representation Classification and Multiorder Feature

This paper presents an algorithm for vehicle detection in high-resolution aerial images through a fast sparse representation classification method and a multiorder feature descriptor that contains information of texture, color, and high-order context. To speed up computation of sparse representation, a set of small dictionaries, instead of a large dictionary containing all training items, is used for classification. To extract the context information of a patch, we proposed a high-order context information extraction method based on the proposed fast sparse representation classification method. To effectively extract the color information, the RGB color space is transformed into color name space. Then, the color name information is embedded into the grids of histogram of oriented gradient feature to represent the low-order feature of vehicles. By combining low- and high-order features together, a multiorder feature is used to describe vehicles. We also proposed a sample selection strategy based on our fast sparse representation classification method to construct a complete training subset. Finally, a set of dictionaries, which are trained by the multiorder features of the selected training subset, is used to detect vehicles based on superpixel segmentation results of aerial images. Experimental results illustrate the satisfactory performance of our algorithm.

Estimation of human body shape and cloth field in front of a kinect

Abstract This paper describes an easy-to-use system to estimate the shape of a human body and his/her clothes. The system uses a Kinect to capture the human׳s RGB and depth information from different views. Using the depth data, a non-rigid deformation method is devised to compensate motions between different views, thus to align and complete the dressed shape. Given the reconstructed dressed shape, the skin regions are recognized by a skin classifier from the RGB images, and these skin regions are taken as a tight constraints for the body estimation. Subsequently, the body shape is estimated from the skin regions of the dressed shape by leveraging a statistical model of human body. After the body estimation, the body shape is non-rigidly deformed to fit the dressed shape, so as to extract the cloth field of the dressed shape. We demonstrate our system and the therein algorithms by several experiments. The results show the effectiveness of the proposed method.