EGO: A Personalised Multimedia Management Tool

The problems of Content-Based Image Retrieval (CBIR) systems can be attributed to the semantic gap between the low-level data representation and the high-level concepts the user associates with images, on the one hand, and the time-varying and often vague nature of the underlying information need, on the other. These problems can be addressed by improving the interaction between the user and the system. In this paper, we sketch the development of CBIR interfaces, and introduce our view on how to solve some of the problems of the studied interfaces. To address the semantic gap and long-term multifaceted information needs, we propose a “retrieval in context” system. EGO is a tool for the management of image collections, supporting the user through personalisation and adaptation. We will describe how it learns from the user’s personal organisation, allowing it to recommend relevant images to the user. The recommendation algorithm is detailed, which is based on relevance feedback techniques.

[1]  Thomas S. Huang,et al.  Relevance feedback in image retrieval: A comprehensive review , 2003, Multimedia Systems.

[2]  Ryen W. White,et al.  An approach for implicitly detecting information needs , 2003, CIKM '03.

[3]  Thomas S. Huang,et al.  Unifying Keywords and Visual Contents in Image Retrieval , 2002, IEEE Multim..

[4]  Thomas S. Huang,et al.  Optimizing learning in image retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[5]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[6]  Jana Urban,et al.  AN ADAPTIVE APPROACH TOWARDS CONTENT-BASED IMAGE RETRIEVAL , 2003 .

[7]  Iain Campbell,et al.  The ostensive model of developing information needs , 2000 .

[8]  G. A. Miller.  THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[9]  Sergios Theodoridis,et al.  Pattern Recognition , 1998, IEEE Trans. Neural Networks.

[10]  Kriengkrai Porkaew,et al.  Query refinement for multimedia similarity retrieval in MARS , 1999, MULTIMEDIA '99.

[11]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[12]  Simone Santini,et al.  Integrated browsing and querying for image databases , 2000, IEEE MultiMedia.

[13]  Simone Santini,et al.  Emergent Semantics through Interaction in Image Databases , 2001, IEEE Trans. Knowl. Data Eng..

[14]  Thomas S. Huang,et al.  ImageGrouper: Search, Annotate and Organize Images by Groups , 2002, VISUAL.

[15]  Joo-Hwee Lim.  Explicit query formulation with visual keywords , 2000, ACM Multimedia.

[16]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[17]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[18]  John C. Dalton,et al.  Hierarchical browsing and search of large image databases , 2000, IEEE Trans. Image Process..

[19]  Philip Chan,et al.  Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms , 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.

[20]  Ben Shneiderman,et al.  MediaFinder: an interface for dynamic personal media management with semantic regions , 2003, CHI Extended Abstracts.

[21]  E. Vesterinen,et al.  Affective Computing , 2009, Encyclopedia of Biometrics.

[22]  R. Manmatha,et al.  Automatic image annotation and retrieval using cross-media relevance models , 2003, SIGIR.

[23]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Ronald Fagin,et al.  Efficient similarity search and classification via rank aggregation , 2003, SIGMOD '03.

[25]  Arthur H. M. ter Hofstede,et al.  Query Formulation as an Information Retrieval Problem , 1996, Comput. J..

[26]  Kerry Rodden,et al.  Does organisation by similarity assist image browsing? , 2001, CHI.

[27]  Sharon Garber,et al.  The art of search: a study of art directors , 1992, CHI.

[28]  Peter Ingwersen,et al.  Information Retrieval Interaction , 1992 .

[29]  Eero Sormunen,et al.  End-User Searching Challenges Indexing Practices in the Digital Newspaper Photo Archive , 2004, Information Retrieval.

[30]  Markus A. Stricker,et al.  Similarity of color images , 1995, Electronic Imaging.

[31]  Joemon M. Jose,et al.  Fetch: A Personalised Information Retrieval Tool , 2004, RIAO.