Trademark image recognition utilizing deep convolutional neural network

M. Haddad*, A. Saleh, Y. Jaber

Department of Electrical Engineering, School of Engineering, The University of Jordan, Amman, Jordan


The purpose of this work is taking into account major challenges of trademark recognition i.e., reduction in semantic gap, attaining more accuracy, reduction in computation complexity by implementing trademark image retrieval through deep Convolutional Neural Networks (CNNs) integrated with relevance feedback mechanism. The dataset features are optimized through Particle swam optimization (PSO); reducing the search space. These best/optimized features are given to the self-organizing map (SOM) for clustering at the preprocessing stage, The CNN model is trained on feature representations of relevant and irrelevant images, using the feedback information from the user bringing the marked relevant images closer to the query. Experimentation proves significant performance by using FlickrLogos-32 PLUS dataset, as illustrated within the performance results section.


Content-based image retrieval, Deep convolutional neural networks, Particle, swarm optimization, Self-organizing map

Digital Object Identifier (DOI)

Article history

Received 12 October 2018, Received in revised form 2 February 2019, Accepted 15 February 2019

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Haddad M, Saleh A, and Jaber Y (2019). Trademark image recognition utilizing deep convolutional neural network. Annals of Electrical and Electronic Engineering, 2(3): 13-20

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