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We present a context-aware hybrid classification system for the problem of fine-grained product class recognition in computer vision. Recently, retail product recognition has become an interesting computer vision research topic. We focus on the classification of products on shelves in a store. This is a very challenging classification problem because many product classes are visually similar in terms...
Statistical topic models represented by Latent Dirichlet Allocation (LDA) and its variants are ubiquitously applied to understanding large corpora. Meanwhile, topic models based on bag-of-words (Bow) rarely adopt contextual information, which encompasses enormous amount of serviceable knowledge in a document, into the probabilistic framework. This shortcoming of LDA leads to its failing to learn contextual...
Image retagging is significant and essential for tag-based applications, such as search and browsing. However, most existing image retagging approaches are typically based on enriching-and-removing and/or reranking strategies, which lead to two drawbacks: 1) since the object and/or human appeared in the images are tagged as individuals, the meanings represented by the mutual context of object and...
The problem of semantic video structuring is vital for automated management of large video collections. The goal is to automatically extract from the raw data the inner structure of a video collection; so that a whole new range of applications to browse and search video collections can be derived out of this high-level segmentation. To reach this goal, we exploit techniques that consider the full...
In this paper, we present a new method for video event recognition based on social roles of agents, which are inferred from their daily activities in continuous video. This is motivated from the observation that people have their social roles, and the information of social roles in certain scene provides useful cues for recognizing video events. First, events are represented by an And-Or Graph (AOG),...
Users are often interested in retrieving only a particular passage on a topic of interest to them. It is therefore necessary to split videos into shorter segments corresponding to appropriate retrieval units. We propose here a method based on a local temporal context for the segmentation of TV news videos into stories. First, we extract multiple descriptors which are complementary and give good insights...
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...
The Undersea Warfare Decision Support System (USW-DSS) will enhance Naval Fleet capabilities by providing a “Common Tactical Picture” between platforms. However, USW-DSS encapsulates a number of mission-critical tasks across many contexts, necessitating solutions to improve multi-tasking and context switching. As part of a Small Business Innovation Research (SBIR) project, we have formulated an approach...
This paper presents a method which able to integrate audio and visual information for human action scene analysis. The approach is top-down for determining and extracting action scenes in video by analyzing both audio and video data. We proposed a framework for recognizing actions by measuring image and action-based information from video with the following characteristics: feature extraction is done...
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