We consider the problem of Content-Based Image Retrieval (CBIR) with interactive user feedback when the user is unable to query the system with natural language text. We employ content-based techniques with Relevance Feedback mechanism to capture the precise need of the user and interactively refine the query. We apply the Exploration/Exploitation trade-off with Hierarchical Gaussian Process Bandits and pseudo feedback in order to tackle the problem of optimization in face of uncertainty and to improve the quality of multiple images selection. We tackle the scalability issue with Self-Organizing Map as a preprocessing techniques. A prototype system called ImSe was developed and tested in experiments with real users in different types of search tasks. The experiments show favorable results and indicate the benefits of proposed aprroach.
|Title of host publication||Information Retrieval - 8th Russian Summer School, RuSSIR 2014, Nizhniy, Novgorod, Russia, August 18-22, 2014, Revised Selected Papers|
|Number of pages||13|
|Publication status||Published - 2014|