QUEST: towards a multi-modal CBIR framework combining query-by-example, query-by-sketch, and text search

The enormous increase of digital image collections urgently necessitates effective, efficient, and in particular highly flexible approaches to image retrieval. Different search paradigms such as text search, query-by-example, or query-by-sketch need to be seamlessly combined and integrated to support different information needs and to allow users to start (and subsequently refine) queries with any type of object. In this paper, we present QUEST (Query by Example, Sketch and Text), a novel flexible multi-modal content-based image retrieval (CBIR) framework. QUEST seamlessly integrates and blends multiple modes of image retrieval, thereby accumulating the strengths of each individual mode. Moreover, it provides several implementations of the different query modes and allows users to select, combine and even superimpose the mode(s) most appropriate for each search task. The combination of search paradigms is by itself done in a very flexible way: either sequentially, where one query mode starts with the result set of the previous one (i.e., for incrementally refining and/or extending a query) or by supporting different paradigms at the same time (e.g., creating an artificial query image by superimposing a query image with a sketch, thereby directly integrating query-by-example and query-by-sketch). We present the overall architecture of QUEST and the dynamic combination and integration of the query modes it supports. Furthermore, we provide first evaluation results that show the effectiveness and the gain in efficiency that can be achieved with the combination of different search modes in QUEST.