Moving people tracking with detection by latent semantic analysis for visual surveillance applications

The latent semantic analysis (LSA) has been widely used in the fields of computer vision and pattern recognition. Most of the existing works based on LSA focus on behavior recognition and motion classification. In the applications of visual surveillance, accurate tracking of the moving people in surveillance scenes, is regarded as one of the preliminary requirement for other tasks such as object recognition or segmentation. However, accurate tracking is extremely hard under challenging surveillance scenes where similarity among multiple objects or occlusion among multiple objects occurs. Usual temporal Markov chain based tracking algorithms suffer from the ‘tracking error accumulation problem’. The accumulated errors can finally make the tracking to drift from the target. To handle the problem of tracking drift, some authors have proposed the idea of using detection along with tracking as an effective solution. However, many of the critical issues still remain unsettled in these detection based tracking algorithms. In this paper, we propose a novel moving people tracking with detection based on (probabilistic) LSA. By employing a novel ‘twin-pipeline’ training framework to find the latent semantic topics of ‘moving people’, the proposed detection can effectively detect the interest points on moving people in different indoor and outdoor environments with camera motion. Since the detected interest points on different body parts can be used to locate the position of moving people more accurately, by combining the detection with incremental subspace learning based tracking, the proposed algorithms resolves the problem of tracking drift during each target appearance update process. In addition, due to the time independent processing mechanism of detection, the proposed method is also able to handle the error accumulation problem. The detection can calibrate the tracking errors during updating of each state of the tracking algorithm. Extensive, experiments on various surveillance environments using different benchmark datasets have proved the accuracy and robustness of the proposed tracking algorithm. Further, the experimental comparison results clearly show that the proposed tracking algorithm outperforms the well known tracking algorithms such as ISL, AMS and WSL algorithms. Furthermore, the speed performance of the proposed method is also satisfactory for realistic surveillance applications.     

基于混合概率潜在语义分析模型的Web聚类

在电子商务应用中,为了更好地了解用户的内在特征,制定有效的营销策略,提出一种基于混合概率潜在语义分析（H PLSA）模型的Web聚类算法。利用概率潜在语义分析（PLSA）技术分别对用户浏览数据、页面内容信息及内容增强型用户事务数据建立PLSA模型, 通过对数—似然函数对三个PLSA模型进行合并得到用户聚类的H PLSA模型和页面聚类的H PLSA模型。聚类分析中以潜在主题与用户、页面以及站点之间的条件概率作为相似度计算依据,聚类算法采用基于距离的k medoids 算法。设计并构建了H PLSA模型,在该模型上对Web聚类算法进行验证,表明该算法是可行的。     

Quantitative cross impact analysis with latent semantic indexing

Cross impact analysis (CIA) consists of a set of related methodologies that predict the occurrence probability of a specific event and that also predict the conditional probability of a first event given a second event. The conditional probability can be interpreted as the impact of the second event on the first. Most of the CIA methodologies are qualitative that means the occurrence and conditional probabilities are calculated based on estimations of human experts. In recent years, an increased number of quantitative methodologies can be seen that use a large number of data from databases and the internet. Nearly 80% of all data available in the internet are textual information and thus, knowledge structure based approaches on textual information for calculating the conditional probabilities are proposed in literature. In contrast to related methodologies, this work proposes a new quantitative CIA methodology to predict the conditional probability based on the semantic structure of given textual information. Latent semantic indexing is used to identify the hidden semantic patterns standing behind an event and to calculate the impact of the patterns on other semantic textual patterns representing a different event. This enables to calculate the conditional probabilities semantically. A case study shows that this semantic approach can be used to predict the conditional probability of a technology on a different technology.     

Prior-based probabilistic latent semantic analysis for multimedia retrieval

Multimedia Tools and Applications - Topic models have shown to be one of the most effective tools in Content-Based Multimedia Retrieval (CBMR). However, the high computational learning cost...     

Multiple level visual semantic fusion method for image re-ranking

Mid-level semantic attributes have obtained some success in image retrieval and re-ranking. However, due to the semantic gap between the low-level feature and intermediate semantic concept, information loss is considerable in the process of converting the low-level feature to semantic concept. To tackle this problem, we tried to bridge the semantic gap by looking for the complementary of different mid-level features. In this paper, a framework is proposed to improve image re-ranking by fusing multiple mid-level features together. The framework contains three mid-level features (DCNN-ImageNet attributes, Fisher vector, sparse coding spatial pyramid matching) and a semi-supervised multigraph-based model that combines these features together. In addition, our framework can be easily extended to utilize arbitrary number of features for image re-ranking. The experiments are conducted on the a-Pascal dataset, and our approach that fuses different features together is able to boost performance of image re-ranking efficiently.     

基于广义内容概率潜在语义分析模型的推荐

针对推荐系统中存在新项目及准确性难以把握等问题,提出一种基于广义内容概率潜在语义模型的推荐方法。该方法以概率潜在语义模型为基础,引入两组潜在变量及项目特征来建立广义内容概率潜在语义模型。该模型中两组潜在变量分别表示用户群体和项目群体,项目特征根据实际情况以特征词的形式进行表示,且通过不对称学习算法完成未知参数的训练及预测。利用三个不同的数据集对所提方法进行实验验证,结果表明该方法具有良好的项目推荐品质。     

一种新的监督潜在语义模型

Sprinkling方法是一种集成了训练样本类别信息的监督潜在语义模型。但是该方法特征权重采用词频,降低了文本分类效果,同时该模型并没有考虑不同样本对分类的贡献能力,而是认为样本对分类的贡献相同,另外,该模型采用多个特征映射一个类别来加强类别知识对分类的贡献。为此,文章在Sprinkling方法的基础上提出了一种新的监督潜在语义模型。实验结果表明,该文方法的总体性能优于原始的Sprinkling方法,在特征数为1 100时,获得了最高分类精度,提高幅度达到1.71%。     

Online belief propagation algorithm for probabilistic latent semantic analysis

Probabilistic latent semantic analysis (PLSA) is a topic model for text documents, which has been widely used in text mining, computer vision, computational biology and so on. For batch PLSA inference algorithms, the required memory size grows linearly with the data size, and handling massive data streams is very difficult. To process big data streams, we propose an online belief propagation (OBP) algorithm based on the improved factor graph representation for PLSA. The factor graph of PLSA facilitates the classic belief propagation (BP) algorithm. Furthermore, OBP splits the data stream into a set of small segments, and uses the estimated parameters of previous segments to calculate the gradient descent of the current segment. Because OBP removes each segment from memory after processing, it is memory-efficient for big data streams. We examine the performance of OBP on four document data sets, and demonstrate that OBP is competitive in both speed and accuracy for online expectation maximization (OEM) in PLSA, and can also give a more accurate topic evolution. Experiments on massive data streams from Baidu further confirm the effectiveness of the OBP algorithm.     

Modeling continuous visual features for semantic image annotation and retrieval

Automatic image annotation has become an important and challenging problem due to the existence of semantic gap. In this paper, we firstly extend probabilistic latent semantic analysis (PLSA) to model continuous quantity. In addition, corresponding Expectation-Maximization (EM) algorithm is derived to determine the model parameters. Furthermore, in order to deal with the data of different modalities in terms of their characteristics, we present a semantic annotation model which employs continuous PLSA and standard PLSA to model visual features and textual words respectively. The model learns the correlation between these two modalities by an asymmetric learning approach and then it can predict semantic annotation precisely for unseen images. Finally, we compare our approach with several state-of-the-art approaches on the Corel5k and Corel30k datasets. The experiment results show that our approach performs more effectively and accurately.     

Automatic text summarization using latent semantic analysis

In the paper, the most state-of-the-art methods of automatic text summarization, which build summaries in the form of generic extracts, are considered. The original text is represented in the form of a numerical matrix. Matrix columns correspond to text sentences, and each sentence is represented in the form of a vector in the term space. Further, latent semantic analysis is applied to the matrix obtained to construct sentences representation in the topic space. The dimensionality of the topic space is much less than the dimensionality of the initial term space. The choice of the most important sentences is carried out on the basis of sentences representation in the topic space. The number of important sentences is defined by the length of the demanded summary. This paper also presents a new generic text summarization method that uses nonnegative matrix factorization to estimate sentence relevance. Proposed sentence relevance estimation is based on normalization of topic space and further weighting of each topic using sentences representation in topic space. The proposed method shows better summarization quality and performance than state-of-the-art methods on the DUC 2001 and DUC 2002 standard data sets.     

基于隐含语义分析的音乐检索

为提高音乐检索效率,使检索结果与搜索目的更接近,提出了基于隐含语义分析的音乐检索方法.将曲谱表示为标准音符和音转的交替串,基于每个交替串使用频率高于包含它的多交替串排列的事实,设计了音乐词汇统计算法.为使各分句能整齐地转化为相同维数的向量,使用最长的分句长度作为标准维数,基于增加频率和的原则进行单词的重新分割.实验结果表明,基于隐含语义分析的检索能获得令人满意的检索结果.     

面向图像语义分割的生成对抗网络模型

图像语义分割一直是计算机视觉中具有挑战性的任务之一。目前多数基于卷积神经网络的语义分割算法存在分割结果不精确,不连续等问题。为了提高图像分割效果,提出了基于生成对抗学习的图像语义分割网络模型。该模型由生成网络和判别网络两部分组成。生成网络含有五个模块,主要作用是生成语义分割图,判别网络与生成网络进行对抗训练,优化生成网络以使生成图像更加接近于Ground Truth。通过在Pascal VOC 2012数据集上对图像进行语义分割的分析,验证了该算法可以有效提高图像语义分割的精度。     

改进的概率潜在语义分析下的文本聚类算法

概率潜在语义分析(PLSA)模型用期望最大化(EM)算法进行参数训练,由于算法参数的随机初始化,致使聚类的效果过度拟合且过分依赖于参数初始值。将潜在语义分析(LSA)模型参数概率化,用以初始化概率潜在语义分析模型的参数,得到的改进算法有效解决了参数随机初始化问题。经实验验证,所提出的方法对文本聚类的归一化互信息(NMI)和准确度都有明显提高。     

A probabilistic semantic model for image annotation and multi-modal image retrieval

This paper addresses automatic image annotation problem and its application to multi-modal image retrieval. The contribution of our work is three-fold. (1) We propose a probabilistic semantic model in which the visual features and the textual words are connected via a hidden layer which constitutes the semantic concepts to be discovered to explicitly exploit the synergy among the modalities. (2) The association of visual features and textual words is determined in a Bayesian framework such that the confidence of the association can be provided. (3) Extensive evaluation on a large-scale, visually and semantically diverse image collection crawled from Web is reported to evaluate the prototype system based on the model. In the proposed probabilistic model, a hidden concept layer which connects the visual feature and the word layer is discovered by fitting a generative model to the training image and annotation words through an Expectation-Maximization (EM) based iterative learning procedure. The evaluation of the prototype system on 17,000 images and 7736 automatically extracted annotation words from crawled Web pages for multi-modal image retrieval has indicated that the proposed semantic model and the developed Bayesian framework are superior to a state-of-the-art peer system in the literature.     

以本体为核心的图像情感语义检索模型

针对目前图像检索系统较难实现情感语义检索的问题,构建了以本体为核心的图像情感语义检索模型。在对核心本体库的构建中,采用将Mpeg-7和概念格理论与本体构建相结合的方法,其难点在于将Mpeg-7标准描述符与图像情感本体的属性构造的结合,以及自动产生新的概念结果的方法;实现了图像情感领域本体框架的半自动构建。通过系统的初步实现与相关实验的验证,证明了此检索模型在实现图像情感语义检索中的可行性和优越性。     

Technology classification with latent semantic indexing

Many national and international governments establish organizations for applied science research funding. For this, several organizations have defined procedures for identifying relevant projects that based on prioritized technologies. Even for applied science research projects, which combine several technologies it is difficult to identify all corresponding technologies of all research-funding organizations. In this paper, we present an approach to support researchers and to support research-funding planners by classifying applied science research projects according to corresponding technologies of research-funding organizations. In contrast to related work, this problem is solved by considering results from literature concerning the application based technological relationships and by creating a new approach that is based on latent semantic indexing (LSI) as semantic text classification algorithm. Technologies that occur together in the process of creating an application are grouped in classes, semantic textual patterns are identified as representative for each class, and projects are assigned to one of these classes. This enables the assignment of each project to all technologies semantically grouped by use of LSI. This approach is evaluated using the example of defense and security based technological research. This is because the growing importance of this application field leads to an increasing number of research projects and to the appearance of many new technologies.     

Automatic document classification based on latent semantic analysis

In this paper, the problem of automatic document classification by a set of given topics is considered. The method proposed is based on the use of the latent semantic analysis to retrieve semantic dependencies between words. The classification of document is based on these dependencies. The results of experiments performed on the basis of the standard test data set TREC (Text REtrieval Conference) confirm the attractiveness of this approach. The relatively low computational complexity of this method at the classification stage makes it possible to be applied to the classification of document streams.