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Identification of characters in TV series and movies is an important and challenging problem. Actor identification results are important information for many higher level multimedia analysis tasks, such as semantic indexing and retrieval, interaction analysis and video summarization. Compared with previous works on actor identification that mainly focus on static features based on face identification and costume detection, our work in this paper attempts to use abundant dynamic information contained in videos to improve the performance when the appearances of actors are hard to detect or changes greatly over time. We propose to mine representative actions of each actor, and show the remarkable power of such actions for actor identification. Videos are firstly divided into shots and represented by Bag of Words (BoW) using spatial-temporal features. Integrating the prototype theory with representativeness, we propose two novel methods to rank the shots and obtain the representative actions. Our methods for actor identification combines representative actions with actors’ appearance. We validate the performance of the proposed methods on episodes of the TV series “The Big Bang Theory”. The experimental results show that the representative actions are consistent with human judgements and can greatly improve the matching performance as complementary to existing handcrafted static features for actor identification. [ABSTRACT FROM AUTHOR]

General natural image deblurring methods do not work well for document images. We exploit a two-tone prior to steer the intermediate latent image towards a piece-wise constant image with only two distinct gray levels. This prior is helpful for the process of kernel estimation to overcome undesirable local minima, and it is not too restrictive to deblur text images with complex backgrounds. Our kernel estimation method comprises two stages, where we first employ contrast-enhancing two-tone prior and then use intermediate-value inhibition regularizer. The resulting optimization formulation is solved by half-quadratic splitting and alternating minimization techniques. The experimental results show that the proposed method is capable of achieving accurate results and compares well with the state-of-the-art. [ABSTRACT FROM AUTHOR]

Auto-encoder—a tricky three-layered neural network, known as auto-association before, constructs the “building block” of deep learning, which has been demonstrated to achieve good performance in various domains. In this paper, we try to investigate the dimensionality reduction ability of auto-encoder, and see if it has some kind of good property that might accumulate when being stacked and thus contribute to the success of deep learning. Based on the above idea, this paper starts from auto-encoder and focuses on its ability to reduce the dimensionality, trying to understand the difference between auto-encoder and state-of-the-art dimensionality reduction methods. Experiments are conducted both on the synthesized data for an intuitive understanding of the method, mainly on two and three-dimensional spaces for better visualization, and on some real datasets, including MNIST and Olivetti face datasets. The results show that auto-encoder can indeed learn something different from other methods. Besides, we preliminarily investigate the influence of the number of hidden layer nodes on the performance of auto-encoder and its possible relation with the intrinsic dimensionality of input data. [ABSTRACT FROM AUTHOR]

Along with the rapid development of digital information technology, video surveillance systems have been widely used in numerous public places, such as squares, shopping malls and banks, to monitor crowd in case of anomalous events. Meanwhile, great challenges have been posed to worldwide researchers because the analysis of the exponentially growing crowd activity data is an arduous task. In this paper, we develop a novel unsupervised crowd activity discovery algorithm aiming to automatically explore latent action patterns among crowd activities and partition them into meaningful clusters. Inspired by the computational model of human vision system, we present a spatio-temporal saliency-based representation to simulate visual attention mechanism and encode human-focused components in an activity stream. Combining with feature pooling, we can obtain a more compact and robust activity representation. Based on affinity matrix of activities, N-cut is performed to generate clusters with meaningful activity patterns. We carry out experiments on our HIT-BJUT dataset and the UMN dataset. The experimental results demonstrate that the proposed unsupervised discovery method is fast and capable of automatically mining meaningful activities from large-scale and unbalanced video data with mixed crowd activities. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks have demonstrated breakthrough accuracies for image classification. A series of feature extractors learned from CNN have been used in other computer vision tasks. However, CNN features of different layers aim to encode different-level information. High-layer features care more about semantic information but less detail information, while low-layer features contain more detail information but suffer from the problem of background clutter and semantic ambiguity. We propose to exploit complementary strengths of different layers in a simple but effective way. A mapping function is designed to highlight the effectiveness of low-layer similarity, when measuring fine-grained similarity between query image and its nearest neighbors with similar semantic. Extensive experiments show that our method can achieve competitive performance on popular retrieval benchmarks. Extensive experiments show that the proposed method outperforms the features extracted from single layers and their direct concatenations. Meanwhile, our method achieves competitive performance on popular retrieval benchmarks. [ABSTRACT FROM AUTHOR]

Multiple object tracking is a key problem for many computer vision applications such as video surveillance, advanced driver assistance or animation. Most of existing tracking-by-detection methods are mainly based on object appearances and motions. However, the contextual information around the target has not been fully exploited. In this paper, we pay more attention to the contextual information and propose an Exchanging Object Context (EOC) model, which takes full advantage of the context information. Specifically, we implement an efficient and accurate online multiple object tracking algorithm with a novel affinity measure to associate detections. This measure calculates the similarity between targets and detections with the background smoothness after exchanging the contexts between detections and targets, using a novel color histogram descriptor. We refine the bounding boxes by measuring the context changes. Extensive experimental results on two public benchmarks demonstrate the effectiveness of the proposed tracking method with comparisons to several state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

View-based 3D object retrieval techniques have become increasingly important in various fields, and lots of ingenious studies have promoted the development of retrieval performance from different aspects. In this paper, we focus on the 2D projective views that represent the 3D objects and propose a boosting approach by evaluating the discriminative ability of each object’s views. The dissimilarity between views’ semantic and discriminative ability is firstly investigated through classification performance. We then propose a simple, robust and effective measurement to study the view’s discriminative ability. By employing the proposed reverse distance metric, we utilize the discriminative information for many to many view set matching. The proposed algorithm is then employed with various features to boost the multi-model graph learning based retrieval method. We compare our approach with several state of the art methods on ETH-80 dataset and National Taiwan University 3D model dataset. The results demonstrate the effectiveness of our method and its excellent boosting performance. [ABSTRACT FROM AUTHOR]