Interactive Content-Based Image Retrieval with Deep Neural Networks.

Recent advances in deep neural networks have given rise to new approaches to content-based image retrieval (CBIR). Their ability to learn universal visual features for any target query makes them a good choice for systems dealing with large and diverse image datasets. However, employing deep neural networks in interactive CBIR systems still poses challenges: either the search target has to be predetermined, such as with hashing, or the computational cost becomes prohibitive for an online setting. In this paper, we present a framework for conducting interactive CBIR that learns a deep, dynamic metric between images. The proposed methodology is not limited to precalculated categories, hashes or clusters of the search space, but rather is formed instantly and interactively based on the user feedback. We use a deep learning framework that utilizes pre-extracted features from Convolutional Neural Networks and learns a new distance representation based on the user's relevance feedback. The experimental results show the potential of applying our framework in an interactive CBIR setting as well as symbiotic interaction, where the system automatically detects what image features might best satisfy the user's needs.