Reconfigurable Peer-to-Peer network Image Retrieval

Performing Content-Based Image Retrieval (CBIR) on Internet connected databases through Peer-to-Peer (P2P) network (P2P-CBIR) effectively explores the large-scale image database distributed over connected peers. In additional to enlarge the retrieval scale from server-client to P2P networks, the required computation and network traffics for performing CBIR can also be distributed. Decentralized unstructured P2P framework is adopted to perform the P2P-CBIR in considering control flexibility, under which reconfiguration is feasible to improve the retrieval performance. Based on this framework, the proposed P2P-CBIR can progressively refine the accuracy of retrieved images when they are transmitted on the routes toward the query peer. With flexible P2P framework, reconfiguration can be carried out on the P2P-CBIR, which helps to improve retrieval efficiency. Experiments verified that the P2P-CBIR with reconfiguration outperforms the one without, and the latter outperforms server-client approaches in both retrieval accuracy and network traffics.

[1]  Rama Chellappa,et al.  An Efficient and Robust Algorithm for Shape Indexing and Retrieval , 2010, IEEE Transactions on Multimedia.

[2]  Irwin King,et al.  Distributed content-based visual information retrieval system on peer-to-peer networks , 2004, TOIS.

[3]  Fei Wang,et al.  Interactive localized content based image retrieval with multiple-instance active learning , 2010, Pattern Recognit..

[4]  Yiannis S. Boutalis,et al.  CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval , 2008, ICVS.

[5]  David Novak,et al.  Building a web-scale image similarity search system , 2010, Multimedia Tools and Applications.

[6]  Miguel Tavares Coimbra,et al.  MPEG-7 Visual Descriptors—Contributions for Automated Feature Extraction in Capsule Endoscopy , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Novella Bartolini,et al.  Quality of Service in Heterogeneous Networks , 2009 .

[8]  Pavel Zezula,et al.  Similarity Searching in Structured and Unstructured P2P Networks , 2009, QSHINE.

[9]  Mustafa Ozden,et al.  A color image segmentation approach for content-based image retrieval , 2007, Pattern Recognit..

[10]  Pavel Zezula,et al.  A Self-Organized System for Content-Based Search in Multimedia , 2008, 2008 Tenth IEEE International Symposium on Multimedia.

[11]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[12]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[13]  James C. French,et al.  Improving Image Retrieval Effectiveness via Multiple Queries , 2003, MMDB '03.