Multiple object localization and recognition has been an important problem in recent years not only because of its difficulty to be time efficient but also due to many different schemes of widespread applications. In many previous works, only a limited amount of object models contribute to less computational time. However, they tend to not work efficiently together with large-scale database. In this paper, we propose a context-aware adaptive window search algorithm and search-based object recognition system to recognize and localize multiple objects in an image with a large-scale database. Since we tackle this problem with the idea that users can get brief information of an item immediately after taking only a snapshot, a low response time is also taken into account. We implement the algorithm within a large-scale book recognition system and present experimental results that demonstrate the efficiency of our algorithm in terms of detection recall, precision, and speed compared to the baseline and efficient subwindow search (ESS) approaches.