In this paper, we are motivated to augment the holistic histogram representation with implicit spatial constrains. To be more concrete, we aim atending a good match function for the problem of object/scene categorizat...
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ISBN:
(纸本)1595937331
In this paper, we are motivated to augment the holistic histogram representation with implicit spatial constrains. To be more concrete, we aim atending a good match function for the problem of object/scene categorization which considers the spatial constraints against heavy clutter and occlusion. Our solution is a partial match kernel under the histogram representation which varies simultaneously at both the feature and spatial resolutions, named as the Feature and Spatial Covariant (FESCO) kernel. Both the FESCO kernel and its late fusion alternative achieve better match accuracy than Spatial Pyramid Match[13] and Pyramid Match[11]. We also apply the keypoint features to video indexing. And on a large scale TRECVID data sets of over 300 hours videos, to our best knowledge, this approach achieves the state-of-the-art result for a single feature. Copyright 2007 ACM.
One of the most exciting aspects of shape modeling is the development of new algorithms and methods to create unusual, interesting and aesthetically pleasing shapes. In this paper, we present an interactive modeling s...
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Semantic video indexing is critical for practical video retrieval systems and a generic and scalable 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 scalable 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 scalability. 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.
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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In this paper, a dynamically constructive method is proposed for proving the universal approximation for single input/single output (SISO) Takagi-Sugeno (T-S) fuzzy systems, which is superior to the existing construct...
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Searching an organization's document repositories for experts is a frequently faced problem in intranet information management. This paper proposes a candidate-centered model which is referred as Candidate Descrip...
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ISBN:
(纸本)9781595938039
Searching an organization's document repositories for experts is a frequently faced problem in intranet information management. This paper proposes a candidate-centered model which is referred as Candidate Description Document (CDD)-based retrieval model. The expertise evidence about an expert candidate scattered over repositories is mined and aggregated automatically to form a profile called the candidate's CDD, which represents his knowledge. We present the model from its foundations through its logical development and argue in favor of this model for expert finding. We devise and compare the different strategies for exploring a variety of expertise evidence. The experiments on TREC enterprise corpora demonstrate that the CDD-based model achieves significant and consistent improvement on performance through comparative studies with non-CDD methods. Copyright 2007 ACM.
Point-Tree Structured Genetic Programming (PTGP) is designed for solving discontinuous function's regression problem. In this paper, we apply this method to identify the discontinuous parameter in a parabolic diff...
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Point-Tree Structured Genetic Programming (PTGP) is designed for solving discontinuous function's regression problem. In this paper, we apply this method to identify the discontinuous parameter in a parabolic differential equation, and then compare the performance of PTGP in the direct regression task and the identification task. The results show that PTGP is a robust problem solver. It can be easily adapted to the identification task and automatically find the way which leads to the optimal solution. In the performance comparison, PTGP performs a little better in the direct regression than the identification. We suggest that it is caused by the complexity of problem itself, that is, the identification task is more complex than the regression task.
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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