Yoda,an adaptive soft classification model: content-based similarity queries and beyond |
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Authors: | Yi-Shin Chen Cyrus Shahabi |
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Affiliation: | (1) Integrated Media Systems Center, Computer Science Department, University of Southern California, CA, USA , US |
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Abstract: | Abstract. Providing a customized result set based upon a user preference is the ultimate objective of many content-based image retrieval
systems. There are two main challenges in meeting this objective: First, there is a gap between the physical characteristics
of digital images and the semantic meaning of the images. Secondly, different people may have different perceptions on the
same set of images. To address both these challenges, we propose a model, named Yoda, that conceptualizes content-based querying
as the task of soft classifying images into classes. These classes can overlap, and their members are different for different
users. The “soft” classification is hence performed for each and every image feature, including both physical and semantic
features. Subsequently, each image will be ranked based on the weighted aggregation of its classification memberships. The
weights are user-dependent, and hence different users would obtain different result sets for the same query. Yoda employs
a fuzzy-logic based aggregation function for ranking images. We show that, in addition to some performance benefits, fuzzy
aggregation is less sensitive to noise and can support disjunctive queries as compared to weighted-average aggregation used
by other content-based image retrieval systems. Finally, since Yoda heavily relies on user-dependent weights (i.e., user profiles)
for the aggregation task, we utilize the users' relevance feedback to improve the profiles using genetic algorithms (GA).
Our learning mechanism requires fewer user interactions, and results in a faster convergence to the user's preferences as
compared to other learning techniques.
Correspondence to: Y.-S. Chen (E-mail: yishinc@usc.edu)
This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC) and IIS-0082826, NIH-NLM R01-LM07061, DARPA and
USAF under agreement nr. F30602-99-1-0524, and unrestricted cash gifts from NCR, Microsoft, and Okawa Foundation. |
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Keywords: | :Image retrieval – Fuzzy logic – Customization – Soft query – Genetic algorithm – Relevance feedback |
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