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CIRES: Content-based Image Retrieval System

This system employs texture analysis via a Gabor filter bank, CIE Lab space color histogram, and structural analysis using perceptual grouping principles.
(http://amazon.ece.utexas.edu/~qasim/qdialog.htm)

CIS: Semantics-based Categorical Image Search and Retrieval

CIS is a semantics-based image search engine developed at Vision Research Lab at UCSB. Instead of using a bottom up method, CIS utilizes a top-down approach to categorize images semantically.
(http://nayana.ece.ucsb.edu/imsearch/imsearch.html)

JSEG: Image Segmentation Demo

Online Segmentation Demo using JSEG algorithm developed at Vision Research Lab at UCSB.
(http://maya.ece.ucsb.edu/JSEG/demo/)

NETRA: A Content-based Image Retrieval System

A Content-Based Image Retrieval System from Vision Research Lab at UCSB combining shape, texture, color, and spatial locations. Image segmentation part is done using the Edgeflow segmentation method.
(http://maya.ece.ucsb.edu/Netra/)

NETRA2: Region-based Retrieval

NETRA 2: A Region-Based Image Retrieval System from Vision Research Lab at UCSB
(http://maya.ece.ucsb.edu/Netra/index2.html)

Online Image Processing Tools

This demo allows you to process your own images via the web. Tools include Stereo images, Segmentation, Zerocrossings, Contourlines, Histogram-Transformations, Color-Separations, Anaglyph Creation, Principal Components.
(http://axon.physik.uni-bremen.de/online_calc)

Pic2segment image segmentation demo

Image segmentation demo from Isis (Intelligent Sensory Information Systems) research group at University of Amsterdam.
(http://zomax.wins.uva.nl:5345/seg_fixed/)

SIMPLIcity

Large-scale content-based image retrieval by James Wang, Jia Li, and Gio Wiederhold. Uses wavelets to Classify web sites and web images.
(http://www-db.stanford.edu/IMAGE)

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