Assessing Image Retrieval Quality at the First Glance.

Abstract

Image retrieval has achieved remarkable improvements with the rapid progress on visual representation and indexing techniques. Given a query image, search engines are expected to retrieve relevant results in which the top-ranked short list is of most value to users. However, it is challenging to measure the retrieval quality on-the-fly without direct user feedbacks. In this paper, we aim at evaluating the quality of retrieval results at the first glance (i.e., with the top-ranked images). For each retrieval result, we compute a correlation based feature matrix that comprises of contextual information from the retrieval list, and then feed it into a convolutional neural network regression model for retrieval quality evaluation. In this proposed framework, multiple visual features are integrated together for robust representations. We optimize the output of this simpleyet- effective evaluation method to be consistent with Discounted Cumulative Gain (DCG), the intuitive measure for the quality of the top-ranked results. We evaluate our method in terms of prediction accuracy and consistency with the ground truth, and demonstrate its practicability in applications such as rank list selection and database image abundance analyses.