Learning from User Behavior in Image Retrieval: Application of Market Basket Analysis

摘要

This article describes an approach to learn feature weights for content-based image retrieval (CBIR) from user interaction log files. These usage log files are analyzed for images marked together by a user in the same query step. The problem is somewhat similar to one of the traditional data mining problems, the market basket analysis problem, where items bought together in a supermarket are analyzed. This paper outlines similarities and differences between the two fields and explains how to use the interaction data for deriving a better feature weighting.