Research on trademark image retrieval based on deep Hashing

doi:10.3969/j.issn.1000-5641.2018.05.015

Abstract

Abstract: Large-scale image retrieval has great potential for a vast number of applications. The fundamentals of image retrieval lie in image feature extraction and high-efficiency similarity evaluation. Deep learning has great capability for feature representation in image objects, while the Hashing technique has better efficiency for high-dimensional data approximation queries. At present, hash learning technology has been widely researched and applied in large-scale image retrieval for the similarity query. This paper extracts trademark image features using convolutional neural network techniques; the data objects are then encoded with bit codes and an approximate query is applied in Hamming space with high efficiency. In this paper, the convolutional neural network is employed and a deep learning based Hash algorithm is proposed; in addition, the loss function and optimizer for the trademark dataset are studied. By obtaining the bit codes that satisfy the Hash coding criterion, the retrieval of trademark data is efficient. Our method can be divided into offline deep Hash learning and online query stages. Experiments are conducted on real trademark data sets, and the results show that our method can obtain high-quality bit code, which has high retrieval accuracy and online query efficiency.

Key words: deep learning, Hash learning, trademark image retrieval, convolutional neural networks, bit encoding

CLC Number:

TP391

YUAN Pei-sen, ZHANG Yong, LI Mei-ling, GU Xing-jian. Research on trademark image retrieval based on deep Hashing[J]. Journal of East China Normal University(Natural Sc, 2018, 2018(5): 172-182.

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