Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback.

User relevance feedback (URF) is emerging as an important component of the multimedia analytics toolbox. State-of-the-art URF systems employ high-dimensional vectors of semantic features and train linear-SVM classifiers in each round of interaction. In a round, they present the user with the most confident media items, which lie furthest from the SVM plane. Due to the scale of current media collections, URF systems must be supported by a high-dimensional index. Usually, these indexes are designed for nearest-neighbour point queries, and it is not known how well they support the URF process. In this paper, we study the performance of four state-of-the-art high-dimensional indexes in the URF context. We analyse the quality of query results, compared to a sequential analysis of the collection, over a range of classifiers, showing that result quality depends (i) heavily on the quality of the SVM classifier and (ii) the index structure itself. We also consider a search-oriented workload, where the goal is to find the first relevant item for a task. The results show that the indexes perform similarly overall, despite differences in their paths to the solution. Interestingly, worse recall can lead to better application-specific performance.