A system is proposed that combines textual and visual statistics in a single index vector for content-based search of a WWW image database. Textual statistics are captured in vector form using latent semantic indexing based on text in the containing HTML document. Visual statistics are captured in vector form using color and orientation histograms. By using an integrated approach, it becomes possible to take advantage of possible statistical couplings between the content of the document (latent semantic content) and the contents of images (visual statistics). The combined approach allows improved performance in conducting content-based search. Search performance experiments are reported for a database containing 350,000 images collected from the WWW.
|Numero di pagine||13|
|Rivista||Computer Vision and Image Understanding|
|Stato di pubblicazione||Published - 1999|
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
La Cascia, M., Cascia, M. L., Taycher, L., Cascia, M. L., Sclaroff, S., & Sethi, S. (1999). Unifying Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web. Computer Vision and Image Understanding, 75, 86-98.