Wei Liang

A Graph Based Subspace Semi-supervised Learning Framework for Dimensionality Reduction

The key to the graph based semi-supervised learning algorithms for classification problems is how to construct the weight matrix of the p-nearest neighbor graph. A new method to construct the weight matrix is proposed and a graph based Subspace Semi-supervised Learning Framework (SSLF) is developed. The Framework aims to find an embedding transformation which respects the discriminant structure inferred from the labeled data, as well as the intrinsic geometrical structure inferred from both the labeled and unlabeled data. By utilizing this framework as a tool, we drive three semi-supervised dimensionality reduction algorithms: Subspace Semi-supervised Linear Discriminant Analysis (SSLDA), Subspace Semi-supervised Locality Preserving Projection (SSLPP), and Subspace Semi-supervised Marginal Fisher Analysis (SSMFA). The experimental results on face recognition demonstrate our subspace semi-supervised algorithms are able to use unlabeled samples effectively.

A novel approach to musical genre classification using probabilistic latent semantic analysis model

A novel approach based on the probabilistic latent semantic analysis model (pLSA) for automatic musical genre classification is proposed in this paper. Unlike traditional usage, the pLSA is used to model musical genre instead of single music signal in the proposed approach. First, an unsupervised clustering algorithm is utilized to group temporal segments in music signals into several natural clusters. By this means, each music signal is decomposed into a bag of “audio words”. Subsequently, the pLSA model of each musical genre is trained through a new iterative training procedure and well-known EM algorithm. This training procedure can iteratively update the pLSA model parameters by discriminatively computing weight of each training music signal and evidently improve the models discriminative performance. Finally, these models can be used to classify new unseen music signals. Experiments on two commonly utilized databases show that our pLSA based approach can give promising results and the iterative learning procedure is effective.

A Chinese Character Localization Method Based on Intergrating Structure and CC-Clustering for Advertising Images

In this paper, a novel Chinese character localization method is proposed for texts in advertising images. To deal with the texts with gradient color, a color clustering method based on edge is introduced to separate the color image into homogeneous color layers. To solve the problem of locating characters varied in size, style and arranged in irregular direction, a novel character localization method is proposed, which integrates structure and CC-clustering to locate characters according to reliable features of characters. Finally, a new noise removal method based on stroke width histogram is employed to remove all non-characters connected components, and then all characters are located. The experimental results show that the proposed method can effectively locate characters in advertising images.

Individualized matching based on logo density for scalable logo recognition

Although many systems based on global or local descriptors have shown promising results for logo recognition, they have handled all logos with the same structure and not considered their diversities. Therefore, with the logo scale increasing, the general way cannot recognize each logo perfectly. To overcome this limitation, we propose a novel strategy to match query and each logo individually using these features. First, a new conception named logo density is introduced as important semantic information for logos. Second, matching density is given according to the logo density and by utilizing it in logistic function an individualized matching strategy is developed to obtain accurate similarity for query and a logo. Finally, we present a fast recognition algorithm based upon bag-of-words model to realize scalable logo recognition. Our method is evaluated on two challenging datasets (our 10,000-class logo dataset and FlickrLogos-27). Experiments demonstrate its superior performance comparing to previous methods.

Spatial connected component pre-locating algorithm for rapid logo detection

This paper introduces a novel pre-locating algorithm for rapid logo detection in unconstrained color images. This work is distinguished by two major contributions. The first is a new method of representation for logo called “spatial connected component descriptor” (SCCD) containing connected component (CC) prediction model and effective-CC pixel distribution histogram. The former represents combinations between CCs based on color and spatial relationships of CCs. While the latter describes the pixel distribution information of effective CCs. The two parts capture the layout of logos from different points. The second is a logo pre-locating algorithm by the means of SCCD to search for logo prediction regions in test images, on which some content-based features are used for logo matching. Experimental results illustrate that our pre-locating algorithm speeds up logo detection to a great extent and shows precise location compared to previous systems.

A Novel Italic Detection and Rectification Method for Chinese Advertising Images

The italic detection and slant rectification is a key step of optical character recognition (OCR). In this paper, a novel method is proposed to detect and rectify italic characters in Chinese advertising images. Based on observations on structures of many characters, the centroid angle is proposed and a statistical study on it is presented. According to the statistical results, the centroid angle of a Chinese character approximately obeys a Gaussian distribution with its slant angle. Moreover, a Markov Random Field (MRF) model, considering the font-face similarity of neighboring characters and the strong correlation between the centroid angle and the slant angle of a character, is then presented to estimate the slant angle of a character. The italic characters can be detected and rectified by the estimated angle. The experimental results demonstrate the proposed method is effective and applicable.

Similarity-based image classification via kernelized sparse representation

We consider the image classification problem based on the similarities between images. The choice of the similarity is related to the particular applications, and it could be based on color, texture, bag-of-features, or even more complex kernels. As long as the pair-wise similarity matrix is transformed into a positive semidefinite one, the similarities of images could be treated as kernels. This transformation makes it possible for kernel methods to solve the similarity-based image classification problem. In this paper, we propose a novel kernelized classification framework based on sparse representation. This new framework casts the classification as finding a sparse linear representation of test image with respect to training images. Unlike the former works, we do this sparse coding procedure through a proposed kernelized orthogonal matching pursuit algorithm, which is performed in inner product space rather than Euclidean space. Through a proper choice of the similarity function, the proposed approach can be applied to diverse image classification problems. Comparative experiments between the proposed method and other existing methods, on two real datasets (Caltech-101 and Face Rec) show that our method performed better.

Subspace Semi-supervised Fisher Discriminant Analysis

Abstract Fisher discriminant analysis (FDA) is a popular method for supervised dimensionality reduction. FDA seeks for an embedding transformation such that the ratio of the between-class scatter to the within-class scatter is maximized. Labeled data, however, often consume much time and are expensive to obtain, as they require the efforts of human annotators. In order to cope with the problem of effectively combining unlabeled data with labeled data to find the embedding transformation, we propose a novel method, called subspace semi-supervised Fisher discriminant analysis (SSFDA), for semi-supervised dimensionality reduction. SSFDA aims to find an embedding transformation that respects the discriminant structure inferred from the labeled data and the intrinsic geometrical structure inferred from both the labeled and unlabeled data. We also show that SSFDA can be extended to nonlinear dimensionality reduction scenarios by applying the kernel trick. The experimental results on face recognition demonstrate the effectiveness of our proposed algorithm.

A Novel Video Classification Method Based on Hybrid Generative/Discriminative Models

We consider the problem of automatically classifying videos into predefined categories based on the analysis of their audio contents. In detail, given a set of labeled videos (such as news, sitcoms, sports, etc.), our objective is to classify a new video into one of these categories. To solve this problem, a novel audio features based video classification method combining an unsupervised generative model named probabilistic Latent Semantic Analysis (pLSA) with a multi-class discriminative classifier is proposed. Since general audio signals usually show complicated distribution in the feature space, k-means clustering method is firstly used to group temporal signal segments with similar low-level features into natural clusters, which are adopted as audio words. Then, the audio stream of a video is decomposed into a bag of audio words. To classify those bags of audio words which extracted from videos, latent topics are discovered by pLSA, and subsequently, training a multi-class classifier on the topic distribution vector for each video. Encouraging classification results have been achieved in our experiments.

A novel location and matching algorithm for rapid logo recognition in video advertisements

This paper introduces a novel location and matching algorithm for rapid logo recognition in video advertisements. This work is distinguished by two major contributions. The first is a new pre-location algorithm to obtain logo prediction regions. The algorithm uses HSV color quantization method to get connected components of the video pictures, and then gains the logo prediction regions based on some aggregative rules. The second is a new index structure for logo matching, using content-based multi-feature fusion, such as HSV block color histogram, CEGCH and representative shape context. Experimental results for video advertisements containing various logos illustrate that our algorithm speeds up logo recognition and shows precise location on our large logo database.