Dynamic exploratory search in content-based image retrieval

Abstract

With the increase of digital media databases, the need for methods that can allow the user to efficiently peruse them has risen dramatically. This paper studies how to explore image datasets more efficiently in online content-based image retrieval (CBIR). We present a new approach for exploratory CBIR that is dynamic, robust and gives a good coverage of the search space, while maintaining a high retrieval precision. Our method uses deep similarity-based learning to find a new representation of the image space. With this metric, it finds the central point of interest and clusters its local region to present the user with representative images within the vicinity of their target search. This clustering provides a more varied training set for the next iteration, allowing the location of relevant features faster. Additionally, relearning a representation of the user’s search interest in each round enables the system to find other non-local regions of interest in the search space, thus preventing the user from getting stuck in a context trap. We test our method in a simulated online setting, taking into consideration the accuracy, coverage and flexibility of adapting to changes in the user’s interest.