An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relevance feedback techniques in image retrieval
Principles of visual information retrieval
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A nearest-neighbor approach to relevance feedback in content based image retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Features for image retrieval: an experimental comparison
Information Retrieval
FCTH: Fuzzy Color and Texture Histogram - A Low Level Feature for Accurate Image Retrieval
WIAMIS '08 Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
Lire: lucene image retrieval: an extensible java CBIR library
MM '08 Proceedings of the 16th ACM international conference on Multimedia
The MIR flickr retrieval evaluation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Overview of the MPEG-7 standard
IEEE Transactions on Circuits and Systems for Video Technology
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Anyone who has ever tried to describe a picture in words is aware that it is not an easy task to find a word, a concept, or a category that characterizes it completely. Most images in real life represent more than a concept; therefore, it is natural that images available to users over the Internet e.g., FLICKR are associated with multiple tags. By the term 'tag', the authors refer to a concept represented in the image. The purpose of this paper is to evaluate the performances of relevance feedback techniques in content-based image retrieval scenarios with multi-tag datasets, as typically performances are assessed on single-tag dataset. Thus, the authors show how relevance feedback mechanisms are able to adapt the search to user's needs either in the case an image is used as an example for retrieving images each bearing different concepts, or the sample image is used to retrieve images containing the same set of concepts. In this paper, the authors also propose two novel performance measures aimed at comparing the accuracy of retrieval results when an image is used as a prototype for a number of different concepts.