active query sensing for mobile location search
While much exciting progress is being made in mobile visual search, one important question has been left unexplored in all current systems. When the first query fails to find the right target (up to 50\% likelihood), how should the user form his/her search strategy in the subsequent interaction? In this paper, we propose a novel Active Query Sensing system to suggest the best way for sensing the surrounding scenes while forming the second query for location search. We accomplish the goal by developing several unique components -- an offline process for analyzing the saliency of the views associated with each geographical location based on score distribution modeling, predicting the visual search precision of individual views and locations, estimating the view of an unseen query, and suggesting the best subsequent view change. Using a scalable visual search system implemented over a NYC street view data set (0.3 million images), we show a performance gain as high as two folds, reducing the failure rate of mobile location search to only 12\% after the second query. This work may open up an exciting new direction for developing interactive mobile media applications through innovative exploitation of active sensing and query formulation.
active query sensing mobile visual search content-based image retrieval mobile location recognition