Given countless web videos available online, one problem is how to help users find videos to their taste in an efficient way. In this paper, to facilitate userpsilas browsing we propose relevant and exploratory recomm...
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Given countless web videos available online, one problem is how to help users find videos to their taste in an efficient way. In this paper, to facilitate userpsilas browsing we propose relevant and exploratory recommendation algorithms utilizing multimodal similarity and contextual network to organize web videos of various topics. Comparison experiments demonstrate proposed approach generates more accurate video relevancy. And our method is more flexible in discovering user latent interests in long tail videos.
Commonsense knowledge plays an important role in various areas such as natural language understanding, information retrieval, etc. This paper presents a method for acquiring commonsense knowledge about properties of c...
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Commonsense knowledge plays an important role in various areas such as natural language understanding, information retrieval, etc. This paper presents a method for acquiring commonsense knowledge about properties of concepts by analyzing how adjectives are used with nouns in everyday language. We firstly mine a large scale corpus for potential concept-property pairs using lexico-syntactic patterns and then filter erroneously acquired ones based on heuristic rules and statistical approaches. For each concept, we automatically select the commonsensical properties and evaluate their applicability. Finally, we generate commonsense knowledge represented with explicit fuzzy quantifiers. Experimental results demonstrate the effectiveness of our approach.
Measuring taxonomic similarity between words plays an important role in many semantic-based applications but still remains a challenging task today. We propose a new method which utilizes restrictive context matrices ...
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Measuring taxonomic similarity between words plays an important role in many semantic-based applications but still remains a challenging task today. We propose a new method which utilizes restrictive context matrices for this problem. We learn a set of special lexico-syntactic patterns automatically and use them to extract taxonomic related contexts of words from raw text. These restrictive contexts are then transformed into real matrices and similarities between them are calculated to reflect the taxonomic similarities between words. The main contribution of our work is that taxonomic related context of words can be mined, evaluated, and used to measure taxonomic similarities between words. Experimental results on Miller-Charles benchmark dataset achieve a correlation coefficient of 0.856.
This paper presents a novel approach based on geodesic distance for sentence similarity computation, which can be used in a query-based information retrieval system. Unlike the traditional distance methods, geodesic d...
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This paper presents a novel approach based on geodesic distance for sentence similarity computation, which can be used in a query-based information retrieval system. Unlike the traditional distance methods, geodesic distance takes into account the spatial relationships of sentences, which better reflects the intrinsic geometric structure of sentence manifold. Experiments demonstrate that the proposed method shows a better correlation to human intuition compared with traditional Euclidean method.
Spatial relation of local image patches plays an important role in object-based image retrieval. An approach called spatial frequent items is proposed as an extension of Bag-of-Words method by introducing spatial rela...
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Spatial relation of local image patches plays an important role in object-based image retrieval. An approach called spatial frequent items is proposed as an extension of Bag-of-Words method by introducing spatial relations between patches. Spatial frequent items are defined as frequent pairs of adjacent local image patches in polar coordinates, and exploited using data mining. Based on these frequent configurations, we develop a method to encode patches and their spatial relations for image indexing and retrieval. Besides, to avoid the interference of background patches, informative patches are filtrated based on their local entropy and self-similarity in the preprocess stage. Experimental results demonstrate that our method can be 8.6% more effective than the state-of-art object retrieval methods.
Video copy detection is essentially a problem of large scale pattern matching. Various copy attacks which change the visual appearance impose hazard on this task. Based on the spatio-temporal consistency, our algorith...
Video copy detection is essentially a problem of large scale pattern matching. Various copy attacks which change the visual appearance impose hazard on this task. Based on the spatio-temporal consistency, our algorithm aims to utilize the invariant pattern of visual information for video matching. Position correlation of trajectory feature points is calculated as the signature for fast detection. Experiments using benchmarked dataset and commonly happened copy attacks verify the robustness and efficiency of our algorithm.
A novel statistical framework for replay detection is presented in this paper. Unlike current methods, the proposed framework exploits both inherent characters and transition relations of replay and non-replay scenes ...
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A novel statistical framework for replay detection is presented in this paper. Unlike current methods, the proposed framework exploits both inherent characters and transition relations of replay and non-replay scenes based on annotation of the video, which realizes segments and classifies video stream into replay and non-replay shots simultaneously. After annotation, the detected replay segment is further verified and its boundaries are adjusted to get more accurate replay segment considering probability distribution of lengths of replay and non-replay shots. Experimental results on soccer video are promising, demonstrating the effectiveness of the proposed framework.
An important problem in text mining is the automatic extraction of semantic relations. The paper provides a domain independent method for automatic extraction of part-whole relations in Chinese corpusa. The method con...
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An important problem in text mining is the automatic extraction of semantic relations. The paper provides a domain independent method for automatic extraction of part-whole relations in Chinese corpusa. The method consists of there phases. First, a set of lexico-syntactical patterns for part-whole relations are designed using known pairs of concepts encoding part-whole relations as seeds, and manually filtering the extracted sentences. Second, Pairs of concepts are extracted using the patterns from a training corpus, which may reflect part-whole relations. Finally, the extracted pairs of concepts are further confirmed using a set of heuristic rules generated based on an analysis of Chinese syntactical and semantic features. Based on a test corpus, the method achieves satisfactory results.
The concept of cluster-degree was put forward and distribute status of particle with different clusterdegree was studied. The reasonable parameters setting range based on cluster-degree was proposed. Under the directi...
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Dynamic description logic (DDL) is among the few emerging service composition solutions through logical reasoning in AI area. To increase DDL reasoning efficiency, we proposed a new DDL-based service composition model...
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Dynamic description logic (DDL) is among the few emerging service composition solutions through logical reasoning in AI area. To increase DDL reasoning efficiency, we proposed a new DDL-based service composition model that supports context-aware service pre-filtering over DDL reasoning space. In this model, service filtering based on contexts runs under the workflow control logic. We evaluated filtering approach on a simple, yet realistic example, and the results shows this approach provides a practical solution.
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