Genre-based search through biomedical images

We exploit the retrieval of visual information from biomedical scientific publication databases. Therefore, we consider the use of domain specific genres to automatically subdivide large image databases into smaller consistent parts. Combination with latent semantic indexing on the picture captions allows for efficient retrieval of images in specific categories. We demonstrate our approach on a large collection of images with captions from the Elsevier Brain Research publications. Initial results demonstrate the power of the proposed combination.

[1]  Richard A. Harshman,et al.  Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..

[2]  Ron Kohavi,et al.  Data mining using /spl Mscr//spl Lscr//spl Cscr/++ a machine learning library in C++ , 1996, Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence.

[3]  Ron Kohavi,et al.  Data Mining Using MLC a Machine Learning Library in C++ , 1996, Int. J. Artif. Intell. Tools.

[4]  Marco La Cascia,et al.  Unifying Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web , 1999, Comput. Vis. Image Underst..

[5]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Arnold W. M. Smeulders,et al.  PicToSeek: combining color and shape invariant features for image retrieval , 2000, IEEE Trans. Image Process..

[7]  David A. Forsyth,et al.  Learning the semantics of words and pictures , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  B. S. Manjunath,et al.  Category-based image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).