Semantic video indexing is critical for practical video retrieval systems and a generic and scalab.e indexing framework is a must for indexing a large semantic lexicon with over 1000 concepts present. This paper fully...
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ISBN:
(纸本)9781595937780
Semantic video indexing is critical for practical video retrieval systems and a generic and scalab.e indexing framework is a must for indexing a large semantic lexicon with over 1000 concepts present. This paper fully explores the idea of incorporating many kinds of diverse features into a single framework, combining them altogether to obtain larger degree of invariance which is absent in any of the component features, and thus achieves genericness and scalab.lity. We scale down the formidable computational expense with a clever design of the classification and fusion schemes. To be specific, ~20 kinds of diverse features are extracted to capture limited yet complementary variance in color, texture and edge with spatial constraints implicitly integrated, and over 100 classifiers are built subsequently and fused to produce a generic detector. The extensive experiments on a total of 310 hours of TRECVID news videos show that the proposed framework yields significantly improved performance over that of the best single feature across a variety of concepts. Moreover, a benchmark comparison demonstrates that this approach is state-of-the-art. Meanwhile, the proposed approach generalizes well over previously unseen programs and stations and scales well to a lexicon of over 300 concepts in the LSCOM [18] ontology. Copyright 2007 ACM.
Though both quantity and quality of semantic concept detection in video are continuously improving, it still remains unclear how to exploit these detected concepts as semantic indices in video search, given a specific...
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ISBN:
(纸本)1595937331
Though both quantity and quality of semantic concept detection in video are continuously improving, it still remains unclear how to exploit these detected concepts as semantic indices in video search, given a specific query. In this paper, we tackle this problem and propose a video search framework which operates like searching text documents. Noteworthy for its adoption of the well-founded text search principles, this framework first selects a few related concepts for a given query, by employing a tf-idf like scheme, called c-tf-idf, to measure the informativeness of the concepts to this query. These selected concepts form a concept subspace. Then search can be conducted in this concept subspace, either by a Vector Model or a Language Model. Further, two algorithms, i.e., Linear Summation and Random Walk through Concept-Link, are explored to combine the concept search results and other baseline search results in a reranking scheme. This framework is both effective and efficient. Using a lexicon of 311 concepts from the LSCOM concept ontology, experiments conducted on the TRECVID 2006 search data set show that: when used solely, search within the concept subspace achieves the state-of-the-art concept search result;when used to rerank the baseline results, it can improve over the top 20 automatic search runs in TRECVID 2006 on average by approx. 20%, on the most significant one by approx. 50%, all within 180 milliseconds on a normal PC. Copyright 2007 ACM.
A new video retrieval paradigm of query-by-concept emerges recently. However, it remains unclear how to exploit the detected concepts in retrieval given a multimedia query. In this paper, we point out that it is impor...
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ISBN:
(纸本)9781595937025
A new video retrieval paradigm of query-by-concept emerges recently. However, it remains unclear how to exploit the detected concepts in retrieval given a multimedia query. In this paper, we point out that it is important to map the query to a few relevant concepts instead of search with all concepts. In addition, we show that solving this problem through both text and image inputs are effective for search, and it is possible to determine the number of related concepts by a language modeling approach. Experimental evidence is obtained on the automatic search task of TRECVID 2006 using a large lexicon of 311 learned semantic concept detectors. Copyright 2007 ACM.
A self-organizing peer-to-peer system is built upon an application level overlay, whose topology is independent of underlying physical network. A well-routed message path in such systems may result in a long delay and...
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In computer graphics, methods for mesh simplification are common. However, most of them focus on static meshes, only few works have been proposed for simplifying deforming surfaces. In this paper, we propose a new met...
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The genetic algorithm (GA) often suffers from the premature convergence because of the loss of population diversity at an early stage of searching. This paper proposes a steep thermodynamical evolutionary algorithm (S...
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Storage system could provide storage space and relevant access operations including allocating storage resources to new object and reclaiming storage space occupied by released objects. Allocation and collection opera...
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Most of the current trust models in peer-to-peer (P2P) systems are identity based, which means that in order for one peer to trust another, it needs to know the other peer's identity. Hence, there exists an inhere...
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This paper focuses on the issue of geographical data's copyrights protection. A Geo-WDBMS has been built by embedding the watermarking functions into the inner code of the open source DBMS PostgreSQL. And its core...
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The unstructured Peer-to-Peer (P2P) systems usually use a "blind search " method to find the requested data object by propagating a query to a number of peers randomly. In order to increase the success rate ...
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