Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Informedia: improving access to digital video
interactions
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Communications of the ACM
A signature for content-based image retrieval using a geometrical transform
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
WALRUS: a similarity retrieval algorithm for image databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Semantic based image retrieval: a probabilistic approach
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting image semantics for picture libraries
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
AMORE: A World Wide Web image retrieval engine
World Wide Web
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The text searching paradigm still prevails even when users are looking for image data for example in the Internet. Searching for images mostly means searching on basis of annotations that have been made manually. When annotations are left empty, which is usually the case, searches on image file names are performed. This may lead to surprising retrieval results. The graphical search paradigm, searching image data by querying graphically, either with an image or with a sketch, currently seems not to be the preferred method partly because of the complexity in designing the query.In this paper we present our PictureFinder system, which currently supports "full image retrieval" in analogy to full text retrieval. PictureFinder allows graphical queries for the image the user has in his mind by sketching colored and/or textured regions or by whole images (query by example). By adjusting the search tolerances for each region and image feature (i.e. hue, saturation, lightness, texture pattern and coverage) the user can tune his query either to find images matching his sketch or images which differing from the specified colors and/or textures to a certain degree. To compare colors we propose a color distance measure that takes into account the fact that different colors spread differently in the color space, and which take into account that the position of a region in an image may be important.Furthermore, we show our query by example approach. Based on the example image chosen by the user, a graphical query is generated automatically and presented to the user. One major advantage of this approach is the possibility to change and adjust a query by example in the same way as a query which was sketched by the user. By deleting unimportant regions and by adjusting the tolerances of the remaining regions the user may focus on image details which are important to him.