A. P. Berman and L. G. Shapiro. "Selecting good keys for triangle-inequality-based pruning algorithms." IEEE International Workshop on Content-Based Access of Image and Video Databases, January 1998.
A. P. Berman and L. G. Shapiro. "A Flexible Image Database System for Content-Based Retrieval." 17th International Conference on Pattern Recognition, August, 1998.
A. P. Berman and L. G. Shapiro. "Triangle-Inequality-Based Pruning Algorithms with Triangle Tries."Proceedings of the SPIE Conference on Storage and Retrieval for Image and Video Databases, January, 1999.
A. P. Berman and L. G. Shapiro, "A Flexible Image Database System for Content-Based Retrieval," Computer Vision and Image Understanding, Vol. 75, Nos. 1-2, 1999, pp. 175-195.
A. P. Berman and L. G. Shapiro, "Efficient Content-Based Retrieval: Experimental Results," Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Databases, June 1999, pp. 55-61.
Demo Flexible Image Database System Using the Groundtruth Database
Our goals for the current year are related to two new aspects of the work:
This project has supported the Ph.D. research of Andrew Berman, who received his degree in March, 1999 and has partially supported the work of Yu-Yu Chou, who received his Ph.D. in December, 1999. Three undergraduate students, Eva Brezin, Kent Schliter, and Marsha Eng, participated in the project during the summers. Yi Li, a 2nd year CSE graduate student, is now supported by the project. Meanwhile, Andrew Berman has founded his own company, QueryPlus, in New Jersey and is working on commercial applications of the funded work. Yu-Yu Chou has just joined Numeritech in San Jose as their senior software engineer.
Researchers in computer vision and computer graphics have developed image distance measures that can compare a sample image or sketch provided by a user to the images in the database and retrieve those that are judged similar by the measure being used. Commercial systems like QBIC and Virage utilize measures that are based on low-level attributes of the image itself, including color histograms, color composition, and texture. State-of-the-art research focuses on more powerful measures that can find regions of an image corresponding to known objects that users wish to retrieve. There has been some success in finding human faces of different selected sizes, human bodies, horses, zebras and other texture animals with known patterns, and such backgrounds as jungles, water, and sky.
Standard database systems, whether they be relational or object-oriented, depend heavily on the ability to index the data according to keywords or key phrases that are stored in the data. While images can be retrieved in this way, it requires human classifiers to look at each image and select a suitable set of keywords. Even if this could be done for millions of images, it would be insufficient, as the keywords would only be one person's ideas of the concepts in that image. Instead, both keywords and a large and powerful set of image distance measures are needed.
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