Rapid object search engine for contextual advertisement

Visual object search, with the goal to find and locate the target object in large image or video collections, is of great interest for many applications and hence has received intensive attentions in recent years. In this demo, we present a spatial context-aware large-scale visual object search system, which is robust to cluttered backgrounds and can well handle scale variations of the objects. Different from the traditional image retrieval systems only matching individual points or fixed-scale spatial contexts, the proposed system considers spatial contexts of varying sizes and shapes, in the form of randomized spatial partition (RSP), and hence provides more accurate search results. Moreover, compared to the computational expensive RANSAC algorithm used in the state-of-the-art retrieval systems, the RSP framework lends our system to easy parallelization and significant speedup for object localization. Consequently, our system works accurately and efficiently. In addition, an Android application has been developed for mobile tasks, by which the user can take a photo of the object he/she wants and then search the same products and their selling information.