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We propose a method for automatic segmentation of categorized objects from a collection of images in the same category, which employs a single auto-context model learned from all images without the need of using pixel level labels. Instead of extracting the salient objects from each image one by one, we extract the objects from all images simultaneously. The segmentation of the salient objects is...
Handwritten text recognition systems commonly combine character classification confidence scores and context models for evaluating candidate segmentation-recognition paths, and the classification confidence is usually optimized at character level. On comparing the performance of class-dependent and class-independent confidence transformation (CT), this paper proposes two regularized class-dependent...
A novel statistical framework for modeling the intrinsic structure of crowded scenes and detecting abnormal activities is presented. The proposed framework essentially turns the complex anomaly detection process into two parts: motion pattern representation and spatio-temporal context modeling. We propose a new 4D spatio-temporal hypervolume representation by integrating the depth constraints to enrich...
In this paper, we propose a novel approach for object detection via foreground feature selection and part-based shape model. It automatically learns a shape model from cluttered training images without need to explicitly given bounding box on objects. Our approach commences by extracting a set of feature descriptors, and iteratively selects the foreground features using Earth Movers Distances based...
It is suggested how a Markov random field can be used for object tracking with context information. The tracking is formulated as a two layer process. In the first phase, the image is represented by a set of feature points which are tracked by a standard tracker. In the second phase, the proposed semi-supervised learning and labeling algorithm is used to label the points to three classes — object,...
We present an ensemble recognition method for graphic symbols that could be interfered by intersecting objects from the context. The symbol is first represented as a set of shape points, each of which is described by a shape context pyramid capturing the local shape characteristics of multi-scale regions surrounding the shape point. A Hough forest ensemble classifier is then employed to learn the...
This paper proposes a novel nonlinear manifold learning method for addressing the ill-posed problem of occluded human action analysis. As we know, a person can perform a broad variety of movements. To capture the multiplicity of a human action, this paper creates a low-dimensional manifold for capturing the intra-path and inter-path contexts of an event. Then, an action path matching scheme can be...
Seeded segmentation methods attempt to solve the segmentation problem in the presence of prior knowledge in the form of a partial segmentation, where a small subset of the image elements (seed-points) have been assigned correct segmentation labels. Common for most of the leading methods in this area is that they seek to find a segmentation where the boundaries of the segmented regions coincide with...
In this paper, we propose a statistical approach for mitosis detection in breast cancer histological images. The proposed algorithm models the pixel intensities in mitotic and non-mitotic regions by a Gamma-Gaussian mixture model and employs a context-aware post-processing in order to reduce false positives. Experimental results demonstrate the ability of this simple, yet effective method to detect...
Shape context has been proven to be an effective method for both local feature matching and global context description. In this paper, we propose a method to build a glocal shape context descriptor in cluttered images. By using the proposed keypoint centered multiple scale edge detection (KMSED) method, glocal shape context encodes fine-scale edges in the keypoint center region while coarse-scale...
This paper proposes a new Probabilistic Graphical Model (PGM) to incorporate the scene, event object interaction and the event temporal contexts into Dynamic Bayesian Networks (DBNs) for event recognition in surveillance videos. We first construct the event DBNs for modeling the events from their own appearance and kinematic observations, and then extend the DBN to incorporate the contexts for boosting...
Detection of moving vehicles in wide area motion imagery (WAMI) is increasingly important, with promising applications in surveillance, traffic scene understanding and public service applications such as emergency evacuation and policy security. However, the large camera motion, along with low contrast between vehicles and backgrounds, makes detection a challenging task. In this paper, we propose...
Video highlight recognition is the procedure in which a long video sequence is summarized into a shorter video clip that depicts the most “salient” parts of the sequence. It is an important technique for content delivery systems and search systems which create multimedia content tailored to their users' needs. This paper deals specifically with capturing highlights inherent to sports videos, especially...
Along with the ever-growing Web, horror video sharing through the Internet has affected our children's psychological health. Most of current horror video filtering researches pay more attention to the extraction of global features or selection of an optimal classifier, while neglecting the underlying contexts in a scene. In this paper, a novel cost-sensitive sparse coding (CSC) model is proposed to...
This paper addresses the problem of shape classification and proposes a method able to exploit peculiarities of both, local and global shape descriptors. In the proposed shape classification framework, the silhouettes of symbols are firstly described through Bags of Shape Contexts. This shape signature is used to solve correspondence problem between points of two shapes. The obtained correspondences...
The locality and sparsity constrained encoding methods have shown the good image classification performance in recent papers. Among these methods, the common strategy is encoding one descriptor into one code by a learned codebook and then applying SPM and Pooling strategy to get the final image representation. However, the ignorance of local spatial context has been a barrier to improve their discriminative...
Recent market report on Consumer Electronics shows that TV with Internet is going to be one of the most demanding products for the near future. As a consequence the demand of recognizing the TV context also increases among the research community. Lots of information about the TV video can be obtained from the meta data in case of digital TV broadcast. But in the developing countries like India, still...
In keyword spotting from handwritten documents, the word similarity is usually computed by combining character similarities. Converting similarity to probabilistic confidence is beneficial for context fusion and threshold selection. In this paper, we propose to directly estimate the posterior probability of candidate characters based on the N-best paths from the segmentation-recognitioin candidate...
In this paper, we introduce the Intuidoc-Loustic Gestures DataBase (ILGDB), a new publicly available database of realistic pen-based gestures for evaluation of recognition systems in pen-enabled interfaces. IL-GDB was collected in a real world context and in an immersive environment. As it contains a large number of unconstrained user-defined gestures, ILGDB offers a unique diversity of content that...
This paper studies the problem of end-to-end windows mining directly from detection output. Traditional object detection systems approach this problem in an ad-hoc manner, say, Non-Maximum Suppression (NMS). Beyond NMS, multi-class context modeling has been explored thoroughly recent years. But all these methods put their emphasis on eliminating false positive windows rather than improving recall...
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