Showing papers by "Dragutin Petkovic published in 1993"

QBIC project: querying images by content, using color, texture, and shape

Abstract: In the query by image content (QBIC) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical (`Give me other images that contain a tumor with a texture like this one'), photo-journalism (`Give me images that have blue at the top and red at the bottom'), and many others in art, fashion, cataloging, retailing, and industry. Key issues include derivation and computation of attributes of images and objects that provide useful query functionality, retrieval methods based on similarity as opposed to exact match, query by image example or user drawn image, the user interfaces, query refinement and navigation, high dimensional database indexing, and automatic and semi-automatic database population. We currently have a prototype system written in X/Motif and C running on an RS/6000 that allows a variety of queries, and a test database of over 1000 images and 1000 objects populated from commercially available photo clip art images. In this paper we present the main algorithms for color texture, shape and sketch query that we use, show example query results, and discuss future directions.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Efficient query by image content for very large image databases

Abstract: The QBIC (query by image content) project in the IBM Almaden Research Center in San Jose, CA, is conducting a theoretical, experimental, and prototyping study of the problem of querying large still image databases efficiently based on image content. Since the problem is difficult, the aim is to discover general principles, but at the same time to identify target application(s) for which concrete pilot systems will be prototyped. A number of algorithms have been developed that allow the user to search based on color, texture, and shape. The search can be focused on either image objects (areas previously outlined by the user) or on the whole images. The search argument can be all or part of a particular image, or user-selected patterns of color, texture, or shape selected from 'pickers', or any weighted combination of these patterns. An example of a search by shape and color, and associated result is given. The results of initial experimentation are encouraging. >

Design and implementation of a low-level image segmentation architecture — LISA

Abstract: The main focus of this paper is on the architectural and implementation issues of a prototype of a low-level image segmentation architecture (LISA). LISA performs real-time (20 Mpixels/sec) gray-level image segmentation, i.e., assignment of image pixels to a few user-selected classes. A decision-theoretic pattern-recognition approach is used, which is divided into a feature extraction part and a decision analysis part. The feature extraction part is based on extracting local and global descriptions for all of the image pixels. In the decision analysis part we designed a novel no-cross-term classifier, which significantly reduced the hardware complexity. The LISA prototype has been built with custom and off-the-shelf VLSI chips. Some measured results will also be reported.