Image Descriptions for Browsing and Retrieval

Abstract : The main thrust of our work under this grant has been the definition of basic image representations that are most appropriate for image retrieval. With the aim of a unified treatment, we have developed the notion of a signature to represent the color, shape, or texture content of an image. We have proposed a new, perceptually motivated metric for signatures, the Earth-Mover's Distance (EMO), and designed sound and efficient algorithms for its computation. Based on these foundational elements we have developed the notion of database navigation as a novel, effective paradigm for interaction with a large database of images. These concepts were the basis for a solid list of publications. We have demonstrated all our ideas with a series of software systems for the retrieval of images from large repertories based on color, texture, shape, and facial features. Eight PhD students have been funded by this project, and three of them have completed their PhD degrees at Stanford with theses on various aspects of image retrieval.

[1]  I. Daubechies Ten Lectures on Wavelets , 1992 .

[2]  I. Daubechies,et al.  Wavelets on the Interval and Fast Wavelet Transforms , 1993 .

[3]  Jia-Guu Leu,et al.  Shape normalization through compacting , 1989, Pattern Recognit. Lett..

[4]  Ramesh C. Jain NSF workshop on Visual Information Management Systems , 1993, SGMD.

[5]  I. G. BONNER CLAPPISON Editor , 1960, The Electric Power Engineering Handbook - Five Volume Set.

[6]  Terry E. Weymouth,et al.  Semantic Queries with Pictures: The VIMSYS Model , 1991, VLDB.

[7]  Pavel Zezula,et al.  Query Processing on Image Databases , 1991, Visual Database Systems.

[8]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Roberto Cipolla,et al.  Extracting the Affine Transformation from Texture Moments , 1994, ECCV.

[10]  Gerard Salton,et al.  Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer , 1989 .

[11]  Theodosios Pavlidis,et al.  Picture Segmentation by a Tree Traversal Algorithm , 1976, JACM.

[12]  Sunil Arya,et al.  Approximate nearest neighbor queries in fixed dimensions , 1993, SODA '93.

[13]  David Salesin,et al.  Multiresolution painting and compositing , 1994, SIGGRAPH.

[14]  Zhi-Qiang Liu,et al.  Filter-based models for pattern classification , 1988, Pattern Recognit..

[15]  Dah-Jye Lee,et al.  Analysis of sequential complex images, using feature extraction and two-dimensional cepstrum techniques , 1989 .