Similarity-Based Image Browsing

Digital images and videos have an increasingly important role in today’s telecommunication and our everyday life in modern information society. The past few years witnessed a proliferation of content-based image retrieval techniques. Images are typically characterized by intrinsic attributes of images such as color, texture, and shape. However, the potential of integrating these techniques with visualization and data-mining techniques has yet been fully explored. Users should be able to explore images in a database or video clips by visual similarities. In this article, we explore the synergy between Pathfinder networks and content-based information retrieval techniques. Salient structures of images are revealed through visualization models derived from features extracted from images. Visualizations are generated from three feature classes of the well-known QBIC system: color, layout, and texture.

[1]  Paul L. Rosin.  Edges: saliency measures and automatic thresholding , 1995, 1995 International Geoscience and Remote Sensing Symposium, IGARSS '95. Quantitative Remote Sensing for Science and Applications.

[2]  Chaomei Chen,et al.  Visualising Semantic Spaces and Author Co-Citation Networks in Digital Libraries , 1999, Inf. Process. Manag..

[3]  Peter Stanchev,et al.  Content-Based Image Retrieval Systems , 2001 .

[4]  S. Sclaroff,et al.  ImageRover: a content-based image browser for the World Wide Web , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

[5]  Dong-Chen He,et al.  Texture Unit, Texture Spectrum, And Texture Analysis , 1990 .

[6]  Gunilla Borgefors,et al.  Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..

[7]  Chaomei Chen.  Generalised similarity analysis and pathfinder network scaling , 1998, Interact. Comput..

[8]  Francis T. Durso,et al.  Network Structures in Proximity Data , 1989 .

[9]  Anil K. Jain,et al.  Image retrieval using color and shape , 1996, Pattern Recognit..

[10]  Paul L. Rosin,et al.  Incorporating shape into histograms for CBIR , 2002, Pattern Recognit..

[11]  James J. Thomas,et al.  Visualizing the non-visual: spatial analysis and interaction with information from text documents , 1995, Proceedings of Visualization 1995 Conference.

[12]  Alex Pentland,et al.  Photobook: tools for content-based manipulation of image databases , 1994, Electronic Imaging.

[13]  Luigi Cinque,et al.  Indexing pictorial documents by their content: a survey of current techniques , 1997, Image Vis. Comput..

[14]  Geoff A. W. West,et al.  Salience Distance Transforms , 1995, CVGIP Graph. Model. Image Process..

[15]  Chaomei Chen,et al.  Information Visualisation and Virtual Environments , 1999 .

[16]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Rin-ichiro Taniguchi,et al.  The depth first picture-expression as an image thresholding strategy , 1989, IEEE Trans. Syst. Man Cybern..

[18]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[19]  James Allan,et al.  Automatic structuring and retrieval of large text files , 1994, CACM.

[20]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[21]  Les Carr,et al.  Trailblazing the literature of hypertext: author co-citation analysis (1989–1998) , 1999, HYPERTEXT '99.

[22]  John R. Smith,et al.  Searching for Images and Videos on the World-Wide Web , 1999 .

[23]  Paul L. Rosin.  Edges: saliency measures and automatic thresholding , 1997, Machine Vision and Applications.