Dehashing: Server-Side Context-Aware Feature Reconstruction for Mobile Visual Search
Journal
IEEE Transactions on Circuits and Systems for Video Technology
Journal Volume
27
Journal Issue
1
Pages
139-148
Date Issued
2017
Author(s)
Kuo Y.-H.
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-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 and videos). Based on the hashed binary codes, we propose a dehashing 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. ? 1991-2012 IEEE.
Subjects
Bag-of-words (BoW); binary codes; mobile visual search (MVS); vector of locally aggregated descriptors (VLAD)
SDGs
Type
journal article
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