CATEGORY-BASED IMAGE RETRIEVAL

S. Newsam, B. Sumengen, and B.S. Manjunath
Department of Electrical and Computer Engineering
University of California, Santa Barbara, CA 93106-9560
{snewsam,sumengen,manj}@ece.ucsb.edu

Abstract

 This work presents a novel approach to content-based
image retrieval in categorical multimedia databases. The
images are indexed using a combination of text and
content descriptors. The categories are viewed as semantic
clusters of images and are used to confine the search
space. Keywords are used to identify candidate categories.
Content-based retrieval is performed in these categories
using multiple image features. Relevance feedback is used
to learn the user's intent- query specification and feature-weighting-
with minimal user-interface abstraction.
The method is applied to a large number of images
collected from a popular categorical structure on the
World Wide Web. Results show that efficient and accurate
performance is achievable by exploiting the semantic
classification represented by the categories. The relevance
feedback loop allows the content descriptor weightings to
be determined without exposing the calculations to the
user.