A Viewable Indexing Structure for the Interactive Exploration of Dynamic and Large Image Collections.

Thanks to the capturing devices cost reduction and the advent of social networks, the size of image collections is becoming extremely huge. Many works in the literature have addressed the indexing of large image collections for search purposes. However, there is a lack of support for exploratory data mining. One may want to wander around the images and experience serendipity in the exploration process. Thus, effective paradigms not only for organising, but also visualising these image collections become necessary. In this article, we present a study to jointly index and visualise large image collections. The work focuses on satisfying three constraints. First, large image collections, up to million of images, shall be handled. Second, dynamic collections, such as ever-growing collections, shall be processed in an incremental way, without reprocessing the whole collection at each modification. Finally, an intuitive and interactive exploration system shall be provided to the user to allow him to easily mine image collections. To this end, a data partitioning algorithm has been modified and proximity graphs have been used to fit the visualisation purpose. A custom web platform has been implemented to visualise the hierarchical and graph-based hybrid structure. The results of a user evaluation we have conducted show that the exploration of the collections is intuitive and smooth thanks to the proposed structure. Furthermore, the scalability of the proposed indexing method is proved using large public image collections.