This paper explores heterogeneous semantic data in Web 1.0, Semantic Web and Web 2.0 for topicspecific crawling and search. A statistical Semantic Association Model (SAM) is proposed to support semantic interoperabili...
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This paper explores heterogeneous semantic data in Web 1.0, Semantic Web and Web 2.0 for topicspecific crawling and search. A statistical Semantic Association Model (SAM) is proposed to support semantic interoperability among four different models of thesauruses, categories, ontologies, and folksonomies. Based on this model, a focused crawling and semantic search framework is developed. In focused crawling of potentially related textual and semantic data, URLs are ordered before crawling and irrelevant Web pages are filtered out after crawling according to SAM-based semantic relevance ranking. In order that the retrieved results are more semantically related to the user queries, approaches of SAM-based semantic query expansion and meta-search result aggregation are designed. Experiments show that the proposed model and framework effectively integrates both keyword data and heterogeneous semantic data for topic-specific crawling and search.
In this paper, we present our solutions for the WikipediaMM task at ImageCLEF 2008. The aim of this task is to investigate effective retrieval approaches in the context of a large-scale and heterogeneous collection of...
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In this paper, we present our solutions for the WikipediaMM task at ImageCLEF 2008. The aim of this task is to investigate effective retrieval approaches in the context of a large-scale and heterogeneous collection of Wikipedia images that are searched by textual queries (and/or sample images and/or concepts) describing a user's information need. We first experimented with a text-based image retrieval approach with query extension, where the expansion terms are automatically selected from a knowledge base that is (semi-)automatically constructed from Wikipedia. We show how this open, constantly evolving encyclopedia can yield inexpensive knowledge structures that are specifically tailored to effectively enhance the semantics of queries. Encouragingly, the experimental results rank in the first place among all submitted runs. The second approach we experimented with is content-based image retrieval (CBIR), in which we first train 1-vs-all classifiers for all query concepts by using the training images obtained by Yahoo! search, and then treat the retrieval task as visual concept detection in the given Wikipedia image set. By comparison, this approach performs better than other submitted CBIR runs. Finally, we experimented with a cross-media image retrieval approach by combining and re-ranking text-based and content-based retrieval results. Despite the final experimental results were not formally submitted before the deadline, this approach performs remarkably better than the text-based retrieval or CBIR approaches.
SVM (Support Vector Machines) is a novel algorithm of machine learning which is based on SLT (Statistical Learning Theory). It can solve the problem characterized by nonlinear, high dimension, small sample and local m...
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Protein is the specific executor of life activities,but there is no protein-based disease network and the current disease networks cannot show that a disease group share the same *** propose a method to construct a pr...
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Protein is the specific executor of life activities,but there is no protein-based disease network and the current disease networks cannot show that a disease group share the same *** propose a method to construct a protein-based network by assigning disease pairs to different intervals according to their similarities and searching for disease groups in each *** methods are used to analyze the disease network,and the result indicates that :in the case where a disease belongs to only one disease group,most diseases have their own protein characteristics,but the common protein of them is not obvious;the more diseases a protein is related to,the more likely the protein becomes common protein;diseases grouping at protein level in this study are different from traditional disease classification;there is a certain relationship between disease symptoms and underlying proteins,but not one-to-one correspondence.
In this paper, a sparse coding based framework is proposed for sign language recognition (SLR), especially for the signer-independent case. To deal with the inter-signer variation, a dictionary capturing the common fe...
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ISBN:
(纸本)9781479983407
In this paper, a sparse coding based framework is proposed for sign language recognition (SLR), especially for the signer-independent case. To deal with the inter-signer variation, a dictionary capturing the common features among different signers is learnt by considering the semantic constraint. Thus for a given sign from an unknown signer, the sparse representation, which maintains more information of this specific sign class while neglecting the identity information as much as possible, can be generated. In our implementation, each sign is partitioned into a fixed number of fragments and the features fusing hand shape and moving trajectory are extracted from the fragments. The dictionary learnt from the training fragments can be taken as the basic subunits of signs and each fragment of sign video can be coded by these basis vectors. Finally, the recognition result is achieved through SVM with the concatenated sparse coding features of the fragments. The experiments and comparisons show that our method is more effective for the signer-independent recognition problem than other baseline methods. At the same time, it also performs well for the signer-dependent case.
Although deep-learning based video recognition models have achieved remarkable success, they are vulnerable to adversarial examples that are generated by adding human-imperceptible perturbations on clean video samples...
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In this paper, we propose a Generalized Unsupervised Manifold Alignment (GU-MA) method to build the connections between different but correlated datasets without any known correspondences. Based on the assumption that...
In this paper, we propose a Generalized Unsupervised Manifold Alignment (GU-MA) method to build the connections between different but correlated datasets without any known correspondences. Based on the assumption that datasets of the same theme usually have similar manifold structures, GUMA is formulated into an explicit integer optimization problem considering the structure matching and preserving criteria, as well as the feature comparability of the corresponding points in the mutual embedding space. The main benefits of this model include: (1) simultaneous discovery and alignment of manifold structures; (2) fully unsuper-vised matching without any pre-specified correspondences; (3) efficient iterative alignment without computations in all permutation cases. Experimental results on dataset matching and real-world applications demonstrate the effectiveness and the practicability of our manifold alignment method.
Recent research has demonstrated that Deep Neural Networks (DNNs) are vulnerable to adversarial patches which introduce perceptible but localized changes to the input. Nevertheless, existing approaches have focused on...
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A new incident source with different angles was constructed for dealing with wide-angle scattering problems. Considering the impendence matrix in method of moments (MOM) is independent from incident angles, the equiva...
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Seam carving is an image resizing method that aims at adapting the image to various display screens while reducing the distortion as much as possible. Severe visual distortion may be introduced by repeated removal or ...
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Seam carving is an image resizing method that aims at adapting the image to various display screens while reducing the distortion as much as possible. Severe visual distortion may be introduced by repeated removal or insertion of seams within a concentrated region of the image. To reduce such visual distortion, we propose a new AESSC method. First, we distribute the energy of each pixel on the seam to its adjacent 8-connected pixels when removing or inserting a seam. Second, since the information of each pixel is anisotropic, we use the Sobel operator to detect the direction that has the maximum edge information and continue the energy accumulation along this direction. Besides, we incorporate a 3D structure consistency constraint in the energy function and adopt a pixel visibility maintenance method. Experimental results show that the proposed method can effectively reduce visual distortion for stereo images while maintaining the geometric consistency.
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