Automatic Detection of Human Nudes
International Journal of Computer Vision - 1998 Marr Prize
Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimedia Tools and Applications
Semantic Modeling and Knowledge Representation in Multimedia Databases
IEEE Transactions on Knowledge and Data Engineering
Virtual Images for Similarity Retrieval in Image Databases
IEEE Transactions on Knowledge and Data Engineering
An Object-Oriented Fuzzy Data Model for Similarity Detection in Image Databases
IEEE Transactions on Knowledge and Data Engineering
Finding Objects by Grouping Primitives
Shape, Contour and Grouping in Computer Vision
A Fuzzy Object Query Language (FOQL) for Image Databases
DASFAA '99 Proceedings of the Sixth International Conference on Database Systems for Advanced Applications
Image Retrieval from the World Wide Web: Issues, Techniques, and Systems
ACM Computing Surveys (CSUR)
A database clustering methodology and tool
Information Sciences—Informatics and Computer Science: An International Journal
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Query-by-content image database will be based on similarity, rater than on matching, where similarity is a measure that is defined and meaningful for every pair of images in the image space. Since it is the human user that, in the end, has to be satisfied with the results of the query, it is natural to base the similarity measure that we will use on the characteristics of human similarity assessment. In the first part of this paper, we review some of these characteristics and define a similarity measure based on them. Another problem that similarity-based databases will have to face is how to combine different queries into a single complex query. We present a solution based on three operators that are the analogous of the and, or, and not operators one uses in traditional databases. These operators are powerful enough to express queries of unlimited complexity, yet have a very intuitive behavior, making easy for the user to specify a query tailored to a particular need.