Negative Correlation Discovery for Big Multimedia Data Semantic Concept Mining and Retrieval

With massive amounts of data producing each day in almost every field, traditional data processing techniques have become more and more inadequate. However, the research of effectively managing and retrieving these big data is still under development. Multimedia high-level semantic concept mining and retrieval in big data is one of the most challenging research topics, which requires joint efforts from researchers in both big data mining and multimedia domains. In order to bridge the semantic gap between high-level concepts and low-level visual features, correlation discovery in semantic concept mining is worth exploring. Meanwhile, correlation discovery is a computationally intensive task in the sense that it requires a deep analysis of very large and growing repositories. This paper presents a novel system of discovering negative correlation for semantic concept mining and retrieval. It is designed to adapt to Hadoop MapReduce framework, which is further extended to utilize Spark, a more efficient and general cluster computing engine. The experimental results demonstrate the feasibility of utilizing big data technologies in negative correlation discovery.

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

[2]  Jun-Wei Hsieh,et al.  Modeling and recognizing action contexts in persons using sparse representation , 2015, J. Vis. Commun. Image Represent..

[3]  Rangasami L. Kashyap,et al.  Generalized Affinity-Based Association Rule Mining for Multimedia Database Queries , 2001, Knowledge and Information Systems.

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

[5]  Haohong Wang,et al.  VideoTopic: Content-Based Video Recommendation Using a Topic Model , 2013, 2013 IEEE International Symposium on Multimedia.

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

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

[8]  Rangasami L. Kashyap,et al.  Augmented transition networks as video browsing models for multimedia databases and multimedia information systems , 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence.

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

[10]  Rangasami L. Kashyap,et al.  Augmented Transition Network as a Semantic Model for Video Data , 2001 .

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

[12]  Shu-Ching Chen,et al.  Efficiently integrating MapReduce-based computing into a Hurricane Loss Projection model , 2013, 2013 IEEE 14th International Conference on Information Reuse & Integration (IRI).

[13]  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.

[14]  Chao Chen,et al.  Weighted Subspace Filtering and Ranking Algorithms for Video Concept Retrieval , 2011, IEEE MultiMedia.

[15]  Georges Quénot,et al.  TRECVID 2015 - An Overview of the Goals, Tasks, Data, Evaluation Mechanisms and Metrics , 2011, TRECVID.

[16]  Chengcui Zhang,et al.  An intelligent framework for spatio-temporal vehicle tracking , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[17]  Chengcui Zhang,et al.  A Dynamic User Concept Pattern Learning Framework for Content-Based Image Retrieval , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[18]  Min Chen,et al.  Spatio-Temporal Analysis for Human Action Detection and Recognition in Uncontrolled Environments , 2015, Int. J. Multim. Data Eng. Manag..

[19]  Shu-Ching Chen,et al.  Feature Selection Using Correlation and Reliability Based Scoring Metric for Video Semantic Detection , 2010, 2010 IEEE Fourth International Conference on Semantic Computing.

[20]  Alekh Jindal,et al.  Hadoop++ , 2010 .

[21]  Xiuqi Li,et al.  Image Retrieval By Color , Texture , And Spatial Information , 2002 .

[22]  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.

[23]  Shu-Ching Chen,et al.  Video Semantic Concept Discovery using Multimodal-Based Association Classification , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[24]  Jun-Wei Hsieh,et al.  Sparse representation for recognizing object-to-object actions under occlusions , 2013, ICIMCS '13.

[25]  Chengcui Zhang,et al.  Innovative Shot Boundary Detection for Video Indexing , 2005 .

[26]  Haohong Wang,et al.  VideoTopic: Modeling User Interests for Content-Based Video Recommendation , 2014, Int. J. Multim. Data Eng. Manag..

[27]  Jun-Wei Hsieh,et al.  Vehicle make and model recognition using sparse representation and symmetrical SURFs , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[28]  Jun-Wei Hsieh,et al.  PLSA-Based Sparse Representation for Object Classification , 2014, 2014 22nd International Conference on Pattern Recognition.

[29]  Min Chen,et al.  Utilizing concept correlations for effective imbalanced data classification , 2014, Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014).

[30]  Scott Shenker,et al.  Shark: SQL and rich analytics at scale , 2012, SIGMOD '13.

[31]  Mei-Ling Shyu,et al.  Weighted Association Rule Mining for Video Semantic Detection , 2010, Int. J. Multim. Data Eng. Manag..

[32]  Rangasami L. Kashyap,et al.  Identifying Overlapped Objects for Video Indexing and Modeling in Multimedia Database Systems , 2001, Int. J. Artif. Intell. Tools.

[33]  Xin Huang,et al.  User Concept Pattern Discovery Using Relevance Feedback And Multiple Instance Learning For Content-Based Image Retrieval , 2002, MDM/KDD.

[34]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[35]  Mei-Ling Shyu,et al.  Sparse Linear Integration of Content and Context Modalities for Semantic Concept Retrieval , 2015, IEEE Transactions on Emerging Topics in Computing.

[36]  T. Lawson,et al.  Spark , 2011 .

[37]  Choochart Haruechaiyasak,et al.  Collaborative Filtering by Mining Association Rules from User Access Sequences , 2005, International Workshop on Challenges in Web Information Retrieval and Integration.

[38]  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.

[39]  Yang Liu,et al.  Enhancing Multimedia Semantic Concept Mining and Retrieval by Incorporating Negative Correlations , 2014, 2014 IEEE International Conference on Semantic Computing.

[40]  Zhao Li,et al.  Multimodal Sparse Linear Integration for Content-Based Item Recommendation , 2013, 2013 IEEE International Symposium on Multimedia.

[41]  Mei-Ling Shyu,et al.  Utilizing Context Information to Enhance Content-Based Image Classification , 2011, Int. J. Multim. Data Eng. Manag..

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