Dehashing Server-Side Side Context Context-Aware Aware Feature Reconstruction for Mobile Visual Search
Abstract: Due to the prevalence of mobile devices, mobile search becomes a more convenient way than desktop search. Different from the traditional desktop search, mobile visual search needs more consideration for the limited resources on mobile devices (e.g., bandwidth, computing power, and memory consumption). The state-of--the-art approaches show that the bag-of of-words (BoW) model is robust for image and video retrieval; however, the large vocabulary tree might not be able to be loaded on the mobile device. We observe that recent works mainly focus on designing compact feature representations on mobile devices for bandwidth-limited limited network (e.g., 3G) and directly adopt feature fea matching on remote servers (cloud). However, the compact (binary) representation might fail to retrieve target objects (images and videos). Based on the hashed binary codes, we propose a dehashing process that reconstructs BoW by leveraging the comput computing ing power of remote servers. To mitigate the information loss from binary codes, we further utilize contextual information (e.g., GPS) to reconstruct a context context-aware aware BoW for better retrieval results. Experiment results show that the proposed method can ach achieve ieve competitive retrieval accuracy as BoW while only transmitting few bits from mobile devices.