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Motion patterns in traffic video can be directly exploited to generate high-level descriptions of video content, which can be used for rule mining and abnormal event detection. The most recent and successful unsupervised methods for complex traffic scene analysis are based on topic models. In this paper, a topic related sparse topical coding framework is proposed for more effectively discovering motion patterns in traffic videos.

In this paper, we introduce a document clustering method based on Sparse Topical Coding, called Cluster-based Sparse Topical Coding. Topic modeling is capable of improving textual document clustering by describing documents via bag-of-words models and projecting them into a topic space. The latent semantic descriptions derived by the topic model can be utilized as features in a clustering process. In our proposed method, document clustering and topic modeling are integrated in a unified framework in order to achieve the highest performance. This framework includes Sparse Topical Coding, which is responsible for topic mining, and K-means that discovers the latent clusters in documents collection. Experimental results on widely-used datasets show that our proposed method significantly outperforms the traditional and other topic model based clustering methods. Our method achieves from 4 to 39% improvement in clustering accuracy and from 2% to more than 44% improvement in normalized mutual information.

With the extensive growth in information, text classification as one of the text mining methods, plays a vital role in organizing and management information. Most text classification methods represent a documents collection as a Bag of Words (BOW) model and then use the histogram of words as the classification features. But in this way, the number of features is very large; therefore performing text classification faces serious computational cost problems. Moreover, the BOW representation is unable to recognize semantic relations between words. Recently, topic-model approaches have been successfully applied for text classification to overcome the problems of BOW. Our main goal in this paper is to investigate the possibility of applying the topic models for Persian text classification and compare between the feature processing techniques of BOW and the topic model based approaches. The experimental results show that the topic-model approach for representing the Persian documents yields at least 9% accuracy improvement compared to the BOW based algorithm.

Traffic density estimation plays an integral role in intelligent transportation systems (ITS), using which provides important information for signal control and effective traffic management. In this paper, we present a new framework for traffic density estimation based on topic model, which is an unsupervised model. This framework uses a set of visual features without any need to individual vehicle detection and tracking, and discovers the motion patterns automatically in traffic scenes by using topic model. Then, likelihood value allocated to each video clip enables us to estimate its traffic density. Results on a standard dataset show high classification performance of our proposed approach and robustness to typical environmental and illumination conditions.

Analyzing motion patterns in traffic videos can directly generate some high-level descriptions of the video content which can be further employed in rule mining and abnormal event detection. The most recent and successful unsupervised approaches for complex traffic scene analysis are based on topic models. However, most existing topic models share some key characteristics which could limit their utility. In this paper, based on extracted optical flow features from video clips, we employ Sparse Topical Coding (STC) framework to automatically discover typical motion patterns in traffic scenes. For this purpose, we improve the STC to overcome one of the drawbacks of topic models with the aim of learning the semantic traffic motion patterns. We go beyond the usual word-document paradigm in topic models by taking into account the order of optical flow words during learning. Experimental results show that our proposed method can learn better motion patterns to analyse the traffic video scenes.

Topic modeling can improve document clustering by projecting documents into a topic space. By document, we mean a general concept. Document can be an image, a video, a textual document or each data which can be described in bag-of-words model based on the histogram of its features. In this paper, we introduce a clustering method based on Sparse Topical Coding (STC). In the proposed method, document clustering and topic modeling are integrated into a unified framework and jointly performed to achieve the best clustering performance. Our method clusters the documents based on STC topic modeling used for mining the topics and K-means clustering used for discovering latent groups in document collection. Experimental results show the effectiveness of our proposed clustering approach.