The Experts below are selected from a list of 2253 Experts worldwide ranked by ideXlab platform
C. Breiteneder - One of the best experts on this subject based on the ideXlab platform.
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An experimental study on the performance of visual information retrieval Similarity models
2002 IEEE Workshop on Multimedia Signal Processing., 2002Co-Authors: H. Eidenberger, C. BreitenederAbstract:This paper is an experimental study on the performance of the two major methods for macro-Level Similarity measurement: linear weighted merging and logical retrieval. Performance is measured as the average query execution time for a significant number of tests. The two models were implemented in the standard version (as they are applied in a number of prototypes) and in an optimized version. The results show that optimized logical retrieval clearly outperforms optimized linear weighted merging.
Carlo Zaniolo - One of the best experts on this subject based on the ideXlab platform.
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MF-Join: Efficient Fuzzy String Similarity Join with Multi-Level Filtering
2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019Co-Authors: Jin Wang, Carlo ZanioloAbstract:As an essential operation in data integration and data cleaning, Similarity join has attracted considerable attention from the database community. In many application scenarios, it is essential to support fuzzy matching, which allows approximate matching between elements that improves the effectiveness of string Similarity join. To describe the fuzzy matching between strings, we consider two Levels of Similarity, i.e., element-Level and record-Level Similarity. Then the problem of calculating fuzzy matching Similarity can be transformed into finding the weighted maximal matching in a bipartite graph. In this paper, we propose MF-Join, a multi-Level filtering approach for fuzzy string Similarity join. MF-Join provides a flexible framework that can support multiple Similarity functions at both Levels. To improve performance, we devise and implement several techniques to enhance the filter power. Specifically, we utilize a partition-based signature at the element-Level and propose a frequency-aware partition strategy to improve the quality of signatures. We also devise a count filter at the record Level to further prune dissimilar pairs. Moreover, we deduce an effective upper bound for the record-Level Similarity to reduce the computational overhead of verification. Experimental results on two popular datasets shows that our proposed method clearly outperforms state-of-the-art methods.
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ICDE - MF-Join: Efficient Fuzzy String Similarity Join with Multi-Level Filtering
2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019Co-Authors: Jin Wang, Carlo ZanioloAbstract:As an essential operation in data integration and data cleaning, Similarity join has attracted considerable attention from the database community. In many application scenarios, it is essential to support fuzzy matching, which allows approximate matching between elements that improves the effectiveness of string Similarity join. To describe the fuzzy matching between strings, we consider two Levels of Similarity, i.e., element-Level and record-Level Similarity. Then the problem of calculating fuzzy matching Similarity can be transformed into finding the weighted maximal matching in a bipartite graph. In this paper, we propose MF-Join, a multi-Level filtering approach for fuzzy string Similarity join. MF-Join provides a flexible framework that can support multiple Similarity functions at both Levels. To improve performance, we devise and implement several techniques to enhance the filter power. Specifically, we utilize a partition-based signature at the element-Level and propose a frequency-aware partition strategy to improve the quality of signatures. We also devise a count filter at the record Level to further prune dissimilar pairs. Moreover, we deduce an effective upper bound for the record-Level Similarity to reduce the computational overhead of verification. Experimental results on two popular datasets shows that our proposed method clearly outperforms state-of-the-art methods.
Hongpeng Wang - One of the best experts on this subject based on the ideXlab platform.
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learning deep multi Level Similarity for thermal infrared object tracking
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Xin Li, Zhenyu He, Di Yuan, Hongpeng WangAbstract:Existing deep Thermal InfraRed (TIR) trackers only use semantic features to describe the TIR object, which lack the sufficient discriminative capacity for handling distractors. This becomes worse when the feature extraction network is only trained on RGB this http URL address this issue, we propose a multi-Level Similarity model under a Siamese framework for robust TIR object tracking. Specifically, we compute different pattern similarities on two convolutional layers using the proposed multi-Level Similarity network. One of them focuses on the global semantic Similarity and the other computes the local structural Similarity of the TIR object. These two similarities complement each other and hence enhance the discriminative capacity of the network for handling distractors. In addition, we design a simple while effective relative entropy based ensemble subnetwork to integrate the semantic and structural similarities. This subnetwork can adaptive learn the weights of the semantic and structural similarities at the training stage. To further enhance the discriminative capacity of the tracker, we construct the first large scale TIR video sequence dataset for training the proposed model. The proposed TIR dataset not only benefits the training for TIR tracking but also can be applied to numerous TIR vision tasks. Extensive experimental results on the VOT-TIR2015 and VOT-TIR2017 benchmarks demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
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Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking
IEEE Transactions on Multimedia, 1Co-Authors: Xin Li, Zhenyu He, Di Yuan, Hongpeng WangAbstract:Existing deep Thermal InfraRed (TIR) trackers only use semantic features to represent the TIR object, which lack the sufficient discriminative capacity for handling distractors. This becomes worse when the feature extraction network is only trained on RGB images. To address this issue, we propose a multi-Level Similarity model under a Siamese framework for robust TIR object tracking. Specifically, we compute different pattern similarities using the proposed multi-Level Similarity network. One of them focuses on the global semantic Similarity and the other computes the local structural Similarity of the TIR object. These two similarities complement each other and hence enhance the discriminative capacity of the network for handling distractors. In addition, we design a simple while effective relative entropy based ensemble subnetwork to integrate the semantic and structural similarities. This subnetwork can adaptive learn the weights of the semantic and structural similarities at the training stage. To further enhance the discriminative capacity of the tracker, we propose a large-scale TIR video sequence dataset for training the proposed model. To the best of our knowledge, this is the first and the largest TIR object tracking training dataset to date. The proposed TIR dataset not only benefits the training for TIR object tracking but also can be applied to numerous TIR visual tasks. Extensive experimental results on three benchmarks demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.
Xiaojie Yuan - One of the best experts on this subject based on the ideXlab platform.
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EIDWT - XML Retrieval with Structural Context Relaxation
2013 Fourth International Conference on Emerging Intelligent Data and Web Technologies, 2013Co-Authors: Xinkun Li, Ying Zhang, Xiaojie YuanAbstract:In this paper, we propose an approach to retrieval XML data with structural context relaxation. We process user query and XML documents as structural term set. Context resemblance is computed based on Level weight of element in context, Level Similarity between elements in longest matched subsequence and other factors. We extend Vector Space Model to answer XML content and structure search. Experiments show that our method has good performance on XML content and structure search.
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XML Retrieval with Structural Context Relaxation
2013 Fourth International Conference on Emerging Intelligent Data and Web Technologies, 2013Co-Authors: Xinkun Li, Ying Zhang, Xiaojie YuanAbstract:In this paper, we propose an approach to retrieval XML data with structural context relaxation. We process user query and XML documents as structural term set. Context resemblance is computed based on Level weight of element in context, Level Similarity between elements in longest matched subsequence and other factors. We extend Vector Space Model to answer XML content and structure search. Experiments show that our method has good performance on XML content and structure search.
H. Eidenberger - One of the best experts on this subject based on the ideXlab platform.
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An experimental study on the performance of visual information retrieval Similarity models
2002 IEEE Workshop on Multimedia Signal Processing., 2002Co-Authors: H. Eidenberger, C. BreitenederAbstract:This paper is an experimental study on the performance of the two major methods for macro-Level Similarity measurement: linear weighted merging and logical retrieval. Performance is measured as the average query execution time for a significant number of tests. The two models were implemented in the standard version (as they are applied in a number of prototypes) and in an optimized version. The results show that optimized logical retrieval clearly outperforms optimized linear weighted merging.