Enhancing Multimedia Semantic Concept Mining and Retrieval by Incorporating Negative Correlations

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 tag-based 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.

[1]  Min Chen,et al.  A unified framework for image database clustering and content-based retrieval , 2004, MMDB '04.

[2]  Shu-Ching Chen,et al.  Correlation-Based Video Semantic Concept Detection Using Multiple Correspondence Analysis , 2008, 2008 Tenth IEEE International Symposium on Multimedia.

[3]  Hsin-Yu Ha,et al.  A Multimedia Semantic Retrieval Mobile System Based on HCFGs , 2014, IEEE MultiMedia.

[4]  Mei-Ling Shyu,et al.  Automatic annotation of drosophila developmental stages using association classification and information integration , 2011, 2011 IEEE International Conference on Information Reuse & Integration.

[5]  Chong-Wah Ngo,et al.  Selection of Concept Detectors for Video Search by Ontology-Enriched Semantic Spaces , 2008, IEEE Transactions on Multimedia.

[6]  Choochart Haruechaiyasak,et al.  Category cluster discovery from distributed WWW directories , 2003, Inf. Sci..

[7]  Paul Over,et al.  Evaluation campaigns and TRECVid , 2006, MIR '06.

[8]  Liang Tang,et al.  Data Mining Meets the Needs of Disaster Information Management , 2013, IEEE Transactions on Human-Machine Systems.

[9]  Min Chen,et al.  A Multiple Instance Learning Approach for Content Based Image Retrieval Using One-Class Support Vector Machine , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[10]  Mei-Ling Shyu,et al.  Semantic Motion Concept Retrieval in Non-Static Background Utilizing Spatial-Temporal Visual Information , 2013, Int. J. Semantic Comput..

[11]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[12]  Luis Fernandez-Luque,et al.  Identifying Measures Used for Assessing Quality of YouTube Videos with Patient Health Information: A Review of Current Literature , 2013, Interactive journal of medical research.

[13]  Chengcui Zhang,et al.  Adaptive background learning for vehicle detection and spatio-temporal tracking , 2003, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint.

[14]  Chao Chen,et al.  Web media semantic concept retrieval via tag removal and model fusion , 2013, ACM Trans. Intell. Syst. Technol..

[15]  Shu-Ching Chen,et al.  Network intrusion detection through Adaptive Sub-Eigenspace Modeling in multiagent systems , 2007, ACM Trans. Auton. Adapt. Syst..

[16]  Shu-Ching Chen,et al.  Effective supervised discretization for classification based on correlation maximization , 2011, 2011 IEEE International Conference on Information Reuse & Integration.

[17]  Alberto Del Bimbo,et al.  Video Annotation and Retrieval Using Ontologies and Rule Learning , 2010, IEEE MultiMedia.

[18]  Everton Alvares Cherman,et al.  Incorporating label dependency into the binary relevance framework for multi-label classification , 2012, Expert Syst. Appl..

[19]  Mei-Ling Shyu,et al.  Biological Image Temporal Stage Classification via Multi-layer Model Collaboration , 2013, 2013 IEEE International Symposium on Multimedia.

[20]  Mei-Ling Shyu,et al.  Model-driven collaboration and information integration for enhancing video semantic concept detection , 2012, 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI).

[21]  John R. Smith,et al.  Multimedia semantic indexing using model vectors , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[22]  K. Ramchandran,et al.  A factor graph framework for semantic indexing and retrieval in video , 2000, 2000 Proceedings Workshop on Content-based Access of Image and Video Libraries.

[23]  Min Chen,et al.  Hierarchical Temporal Association Mining for Video Event Detection in Video Databases , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.

[24]  K. Pearson.  VII. Note on regression and inheritance in the case of two parents , 1895, Proceedings of the Royal Society of London.

[25]  Min Chen,et al.  Video Semantic Event/Concept Detection Using a Subspace-Based Multimedia Data Mining Framework , 2008, IEEE Transactions on Multimedia.

[26]  Xiuqi Li,et al.  An effective content-based visual image retrieval system , 2002, Proceedings 26th Annual International Computer Software and Applications.

[27]  Shu-Ching Chen,et al.  Association rule mining with a correlation-based interestingness measure for video semantic concept detection , 2012, Int. J. Inf. Decis. Sci..

[28]  Min Chen,et al.  Image database retrieval utilizing affinity relationships , 2003, MMDB '03.

[29]  Mei-Ling Shyu,et al.  Leveraging Concept Association Network for Multimedia Rare Concept Mining and Retrieval , 2012, 2012 IEEE International Conference on Multimedia and Expo.

[30]  Min Chen,et al.  A multimodal data mining framework for soccer goal detection based on decision tree logic , 2006, Int. J. Comput. Appl. Technol..

[31]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[32]  Chong-Wah Ngo,et al.  Domain adaptive semantic diffusion for large scale context-based video annotation , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[33]  Chao Chen,et al.  A Web-Based Multimedia Retrieval System with MCA-Based Filtering and Subspace-Based Learning Algorithms , 2013, Int. J. Multim. Data Eng. Manag..

[34]  Rangasami L. Kashyap,et al.  Indexing and searching structure for multimedia database systems , 1999, Electronic Imaging.