De-Hashing: Server-Side Context-Aware Feature Reconstruction for Mobile Visual Search
This addresses bandwidth and computational constraints in mobile visual search, though it appears incremental as it builds on existing hashing and context-aware methods.
The paper tackles the problem of mobile visual search by proposing a de-hashing process that reconstructs bag-of-words features from binary codes on remote servers, achieving competitive retrieval accuracy while transmitting only a few bits from mobile devices.
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 bag-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 network (e.g., 3G) and directly adopt feature matching on remote servers (cloud). However, the compact (binary) representation might fail to retrieve target objects (images, videos). Based on the hashed binary codes, we propose a de-hashing process that reconstructs BoW by leveraging the computing power of remote servers. To mitigate the information loss from binary codes, we further utilize contextual information (e.g., GPS) to reconstruct a context-aware BoW for better retrieval results. Experiment results show that the proposed method can achieve competitive retrieval accuracy as BoW while only transmitting few bits from mobile devices.