A Web-Based Multimedia Retrieval System with MCA-Based Filtering and Subspace-Based Learning Algorithms

Lin, Lin; Chen, Chao; Meng, Tao

doi:10.4018/jmdem.2013040102

Cited by 3 publications

(1 citation statement)

References 73 publications

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“…For example, Ballan et al [24] combined the linguistic ontology from WordNet [25] and association rule mining to help video retrieval. Similar studies are presented in [1] [26][27] [28].…”

Section: Introductionsupporting

confidence: 66%

Enhancing Multimedia Semantic Concept Mining and Retrieval by Incorporating Negative Correlations

Meng

Liu

Shyu

et al. 2014

2014 IEEE International Conference on Semantic Computing

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In recent years, we have witnessed a deluge of multimedia data such as texts, images, and videos. However, the research of managing and retrieving these data efficiently is still in the development stage. The conventional tagbased searching approaches suffer from noisy or incomplete tag issues. As a result, the content-based multimedia data management framework has become increasingly popular. In this research direction, multimedia high-level semantic concept mining and retrieval is one of the fastest developing research topics requesting joint efforts from researchers in both data mining and multimedia domains. To solve this problem, one great challenge is to bridge the semantic gap which is the gap between high-level concepts and low-level features. Recently, positive inter-concept correlations have been utilized to capture the context of a concept to bridge the gap. However, negative correlations have rarely been studied because of the difficulty to mine and utilize them. In this paper, a concept mining and retrieval framework utilizing negative inter-concept correlations is proposed. Several research problems such as negative correlation selection, weight estimation, and score integration are addressed. Experimental results on TRECVID 2010 benchmark data set demonstrate that the proposed framework gives promising performance.

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“…For example, Ballan et al [24] combined the linguistic ontology from WordNet [25] and association rule mining to help video retrieval. Similar studies are presented in [1] [26][27] [28].…”

Section: Introductionsupporting

confidence: 66%

Enhancing Multimedia Semantic Concept Mining and Retrieval by Incorporating Negative Correlations

Meng

Liu

Shyu

et al. 2014

2014 IEEE International Conference on Semantic Computing

Self Cite

View full text Add to dashboard Cite

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Weighted subspace modeling for semantic concept retrieval using gaussian mixture models

2016

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Gaussian Mixture Model-Based Subspace Modeling for Semantic Concept Retrieval

Chen

Shyu

Chen

2015

2015 IEEE International Conference on Information Reuse and Integration

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Data mining and machine learning methods have been playing an important role in searching and retrieving multimedia information from all kinds of multimedia repositories. Although some of these methods have been proven to be useful, it is still an interesting and active research area to effectively and efficiently retrieve multimedia information under difficult scenarios, i.e., detecting rare events or learning from imbalanced datasets. In this paper, we propose a novel subspace modeling framework that is able to effectively retrieve semantic concepts from highly imbalanced datasets. The proposed framework builds positive subspace models on a set of positive training sets, each of which is generated by a Gaussian Mixture Model (GMM) that partitions the data instances of a target concept (i.e., the original positive set of the target concept) into several subsets and later merges each subset with the original positive data instances. Afterwards, a joint-scoring method is proposed to fuse the final ranking scores from all such positive subspace models and the negative subspace model. Experimental results evaluated on a public-available benchmark dataset show that the proposed subspace modeling framework is able to outperform peer methods commonly used for semantic concept retrieval.

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A Web-Based Multimedia Retrieval System with MCA-Based Filtering and Subspace-Based Learning Algorithms

Cited by 3 publications

References 73 publications

Enhancing Multimedia Semantic Concept Mining and Retrieval by Incorporating Negative Correlations

Enhancing Multimedia Semantic Concept Mining and Retrieval by Incorporating Negative Correlations

Weighted subspace modeling for semantic concept retrieval using gaussian mixture models

Gaussian Mixture Model-Based Subspace Modeling for Semantic Concept Retrieval

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