Social Relevance Feedback Based on Multimedia Content Power

This paper proposes a novel social media relevance feedback algorithm, based on multimedia content power (MCP). The algorithm estimates in a recursive manner, the similarity measure. This is accomplished by using a set of relevant/irrelevant samples, which are provided by the user, in order to adjust the system’s response. In particular, the similarity measure is expressed in a parametric form of functional components. Another innovative point has to do with the estimation of MCP, which measures the influence of files over social media users. Toward this direction, user interactions (e.g., comments, likes, and shares) indicate that the file is influencing to them. The algorithm takes into consideration both the visual characteristics of multimedia files and their influence to retrieve information. The experimental results show that the proposed scheme offers several merits and future work is also discussed.

[1]  Sunju Park,et al.  Determining Content Power Users in a Blog Network: An Approach and Its Applications , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[2]  Lorenzo Bruzzone,et al.  A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrieval , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Philip S. Yu,et al.  Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns , 2011, IEEE Transactions on Knowledge and Data Engineering.

[4]  Xiangyang Wang,et al.  An image retrieval scheme with relevance feedback using feature reconstruction and SVM reclassification , 2014, Neurocomputing.

[5]  Chiou-Ting Hsu,et al.  Image retrieval with relevance feedback based on graph-theoretic region correspondence estimation , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[6]  Levent Onural,et al.  Image sequence analysis for emerging interactive multimedia services-the European COST 211 framework , 1998, IEEE Trans. Circuits Syst. Video Technol..

[7]  J. Shanbehzadeh,et al.  Relevance Feedback Optimization in Content Based Image Retrieval Via Enhanced Radial Basis Function Network , 2022 .

[8]  Oren Kurland,et al.  Utilizing relevance feedback in fusion-based retrieval , 2014, SIGIR.

[9]  Christos Faloutsos,et al.  MindReader: Querying Databases Through Multiple Examples , 1998, VLDB.

[10]  Zhang Xiong,et al.  3D Object Retrieval With Multitopic Model Combining Relevance Feedback and LDA Model , 2015, IEEE Transactions on Image Processing.

[11]  Barry Smyth,et al.  On the real-time web as a source of recommendation knowledge , 2010, RecSys '10.

[12]  Teruko Mitamura,et al.  Zero-Example Event Search using MultiModal Pseudo Relevance Feedback , 2014, ICMR.

[13]  Stefanos D. Kollias,et al.  Mining User Queries with Markov Chains: Application to Online Image Retrieval , 2013, IEEE Transactions on Knowledge and Data Engineering.

[14]  Thomas Sikora,et al.  The MPEG-7 visual standard for content description-an overview , 2001, IEEE Trans. Circuits Syst. Video Technol..

[15]  Stefanos D. Kollias,et al.  A stochastic framework for optimal key frame extraction from MPEG video databases , 1999, 1999 IEEE Third Workshop on Multimedia Signal Processing (Cat. No.99TH8451).

[16]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[17]  Hsinchun Chen,et al.  Social Media Analytics and Intelligence , 2010, IEEE Intell. Syst..

[18]  E. Kreyszig.  Introductory Functional Analysis With Applications , 1978 .

[19]  A. Lakshmi,et al.  Long Term Relevance Feedback: A Probabilistic Axis Re-Weighting Update Scheme , 2015, IEEE Signal Processing Letters.

[20]  Jorma Laaksonen,et al.  Pinview: Implicit Feedback in Content-Based Image Retrieval , 2010, WAPA.

[21]  Barry Smyth,et al.  Using twitter to recommend real-time topical news , 2009, RecSys '09.

[22]  Nikolaos D. Doulamis,et al.  Evaluation of relevance feedback schemes in content-based in retrieval systems , 2006, Signal Process. Image Commun..

[23]  John Hannon,et al.  Recommending twitter users to follow using content and collaborative filtering approaches , 2010, RecSys '10.

[24]  John Shawe-Taylor,et al.  Content-based Image Retrieval with Multinomial Relevance Feedback , 2010, ACML.

[25]  Nikolaos D. Doulamis,et al.  Generalized nonlinear relevance feedback for interactive content-based retrieval and organization , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[26]  G. Lewicki,et al.  Approximation by Superpositions of a Sigmoidal Function , 2003 .

[27]  Stefanos D. Kollias,et al.  A fuzzy video content representation for video summarization and content-based retrieval , 2000, Signal Process..

[28]  G. Homans,et al.  Social Behavior: Its Elementary Forms. , 1961 .

[29]  Stefanos D. Kollias,et al.  Efficient summarization of stereoscopic video sequences , 2000, IEEE Trans. Circuits Syst. Video Technol..

[30]  Dacheng Tao,et al.  Biased Discriminant Euclidean Embedding for Content-Based Image Retrieval , 2010, IEEE Transactions on Image Processing.

[31]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.