An SUI-based approach to explore visual search results cluster-graphs

Nowadays, exponential growth in online production and extensive perceptual power of visual contents (i.e., images) complicate the users’ information needs. The research has shown that users are interested in satisfying their visual information needs by accessing the image objects. However, the exploration of images via existing search engines is challenging. Mainly, existing search engines employ linear lists or grid layouts, sorted in descending order of relevancy to the user’s query to present the image results, which hinders image exploration via multiple information modalities associated with them. Furthermore, results at lower-ranking positions are cumbersome to reach. This research proposed a Search User Interface (SUI) approach to instantiate the non-linear reachability of the image results by enabling interactive exploration and visualization options. We represent the results in a cluster-graph data model, where the nodes represent images and the edges are multimodal similarity relationships. The results in clusters are reachable via multimodal similarity relationships. We instantiated the proposed approach over a real dataset of images and evaluated it via multiple types of usability tests and behavioral analysis techniques. The usability testing reveals good satisfaction (76.83%) and usability (83.73%) scores.