This abstract paper sketches our research towards Struc-tured Semantic Embedding of multimedia data. Though a tag may have multiple senses with completely different visual imagery, current semantic embedding methods r...
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Existing research on extreme value query in wireless sensor networks is mainly focus on finding out sensors with highest metric. Yet in most actually scenarios, people cares more about special network regions than det...
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This paper presents a reference framework, called BUD, to manage a large shared bank of unstructured data. This paper lists several important issues on managing or maintaining the unstructured data in BUD. BUD stores ...
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Representing images by bag of visual codes (BoVC) features has been the cornerstone of state-of-the-art image classification system. Since the BoVC features depend on a precomputed codebook in use, when the codebook a...
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In recent years MapReduce has risen to be the de-facto tool for big data processing. MapReduce is a disruptive innovation. It has changed the landscape of database market, the landscape of technologies, as well as the...
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In this paper, based on concept lattices and dual concept lattices, we introduced a pair of rough set approximation operators within formal contexts. The proposed approximations operators don't require the equival...
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In this paper, based on concept lattices and dual concept lattices, we introduced a pair of rough set approximation operators within formal contexts. The proposed approximations operators don't require the equivalence relation any more. The properties of the proposed approximation operators are discussed in details.
Given the proliferation of geo-tagged images, the question of how to exploit geo tags and the underlying geo context for visual search is emerging. Based on the observation that the importance of geo context varies ov...
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In this paper we describe our image annotation system par ticipated in the ImageCLEF 2013 scalable concept image annotation task. The system leverages multiple base classifiers, including single feature and multi-feat...
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In this paper we describe our image annotation system par ticipated in the ImageCLEF 2013 scalable concept image annotation task. The system leverages multiple base classifiers, including single feature and multi-feature kNN classifiers and histogram intersection ker nel SVMs, all of which are learned from the provided 250K web images and provided features with no extra manual verification. These base clas sifiers are combined into a stacked model, with the combination weights optimized to maximize the geometric mean of F-samples, F-concepts, and AP-samples metrics on the provided development set. By varying the configuration of the system, we submitted five runs. Evaluation re sults show that for all of our runs, model stacking with optimized weights performs best. Our system can annotate diverse Internet images purely based on the visual content, at the following accuracy level: F-samples of 0.290, F-concepts of 0.304, and AP-samples of 0.380. What is more, a system-to-system comparison reveals that our system and the best sub mission this year are complementary with respect to the best annotated concepts, suggesting the potential for future improvement.
In this paper, we study how to perform XML query expansion effectively from the high quality pseudo-relevance documents. A solution for selecting good expansion information is presented, in which various features impa...
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In this paper, we study how to perform XML query expansion effectively from the high quality pseudo-relevance documents. A solution for selecting good expansion information is presented, in which various features impacting weight, such as term element frequency, term inverse element frequency, semantic weight of tag and level information, are analyzed and those term with high weigh value are selected as expansion term. Experiment results show that proposed expansion method is feasible. Compared to original query and traditional expansion method with no structure features considered, our method achieves better retrieval performance.
Ontology matching determines the correspondences between concepts and relations of related ontologies. In this paper, we put forward an ontology hierarchies matching approach based on lattices alignment. The proposed ...
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Ontology matching determines the correspondences between concepts and relations of related ontologies. In this paper, we put forward an ontology hierarchies matching approach based on lattices alignment. The proposed lattice-based matching algorithm can be utilized not only in matching processes between two ontologies, but also in annotation processes between an ontology and its corresponding resources. Experiments on spatiotemporal ontology annotation have been carried out which shown the applicability of the approach.
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