Strategic directions in human-computer interaction
ACM Computing Surveys (CSUR) - Special ACM 50th-anniversary issue: strategic directions in computing research
A novel relevance feedback technique in image retrieval
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Access to Multimedia Information: From Technology Trends to State of the Art
Content-Based Access to Multimedia Information: From Technology Trends to State of the Art
Retrieval of Still Images by Content
ESSIR '00 Proceedings of the Third European Summer-School on Lectures on Information Retrieval-Revised Lectures
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We propose an image retrieval methodology for a collection of similar images. By similar, we mean that one can define, for the collection, a set of dimensions, and for each of which a set of features. The dimensions are used to capture the essential characteristics of the images in the collection, and the features are for describing each image to a certain degree. We call this strategy fine-grained image retrieval to differentiate it from the more common coarse-grained retrieval, which does not assume any semantic properties on the image collection. The effectiveness of our methodology is demonstrated through an icon-based interactive retrieval system on a collection of butterfly images. This system provides the user with a friendly initial query-by-feature (QBF) interface. The user can then use query-by-example (QBE) to refine the query. In addition to presenting an outline of the methodology and the implementation on butterfly images, we also present some experimental results.