Image Retrieval with Relevance Feedback using SVM Active Learning

Abstract

In content-based image retrieval, relevant feedback is studied extensively
to narrow the gap between low-level image feature and high-level semantic
concept. In general, relevance feedback aims to improve the retrieval
performance by learning with user's judgements on the retrieval results.
Despite widespread interest, but feedback related technologies are often
faced with a few limitations. One of the most obvious limitations is often
requiring the user to repeat a number of steps before obtaining the
improved search results. This makes the process inefficient and tedious
search for the online applications. In this paper, a effective feedback
related scheme for content-based image retrieval is proposed. First, a
decision boundary is learned via Support Vector Machine to filter the
images in the database. Then, a ranking function for selecting the most
informative samples will be calculated by defining a novel criterion that
considers both the scores of Support Vector Machine function and similarity
metric between the "ideal query" and the images in the database. The
experimental results on standard datasets have showed the effectiveness
of the proposed method.