Title: Mobile visual search algorithm based on improved VGG-F and hash with application in IoT

Authors: Shanshan Ji; Jianxin Li; Jie Liu; Wenliang Cao; Bin Li; Fei Jiang; Yang Liu

Addresses: School of Artificial Intelligence, Dongguan Polytechnic, Dongguan 523808, China ' School of Electronic Information, Dongguan Polytechnic, Dongguan 523808, China ' School of Electronic Information, Dongguan Polytechnic, Dongguan 523808, China ' School of Electronic Information, Dongguan Polytechnic, Dongguan 523808, China ' School of Electronic Information, Dongguan Polytechnic, Dongguan 523808, China ' School of Modern Circulation, Guangxi International Business Vocational College, Nanning 530000, China ' School of Electronic Information, Dongguan Polytechnic, Dongguan 523808, China

Abstract: This paper explores the use of deep learning based hash method in IoT to build a more powerful and real-time mobile visual search, and proposes a lightweight, low latency and high-precision mobile visual search algorithm based on deep hash method and its application in IoT. This paper discusses two aspects: image semantic feature extraction and fast retrieval. Based on the deep learning method and hash method, an image semantic feature extraction model for Library Digital Humanities is constructed, and the loss function applicable to the field is constructed. The model training and MVS retrieval process experiment are carried out through the Library Data set, and this method is applied to the library IoT system. In order to verify the effectiveness of our method, we compared the proposed method with VHB, SSFS, DLBH, SSDH and other methods. Experimental results show that the proposed method is more effective and robust than the existing methods.

Keywords: IoT; deep learning; hash function; VGG-F; mobile visual search; library digital humanities.

DOI: 10.1504/IJGUC.2023.131009

International Journal of Grid and Utility Computing, 2023 Vol.14 No.2/3, pp.94 - 106

Received: 22 Jun 2022
Received in revised form: 06 Sep 2022
Accepted: 18 Sep 2022
Published online: 18 May 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article