Trademark classification by shape using ensemble of RBFNNs

Lai, Weiwei; Ng, Wing W. Y.; Chan, Patrick P. K.; Yeung, Daniel

doi:10.1109/icmlc.2010.5581030

Cited by 1 publication

(1 citation statement)

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“…In [13], nine feature vectors of low-order color moments in HSV color space were combined with low-orders Hu moments and eccentricity which were extracted from gray shape-region image by Rui and Huang's (1998) technique. Recently, [8] employed 10 invariant moments as shape features of trademark images. The equidistance based on concentric circles [7] was used to partition region, labelled the first step in [2] and [3].…”

Section: Introductionmentioning

confidence: 99%

Trademark image retrieval based on scale, rotation, translation invariant features

Nguyễn¹,

Nguyen²,

2013

The 2013 RIVF International Conference on Computing &Amp; Communication Technologies - Research, Innovation, and Vision for Fut

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Trademark registration offices or authorities have been bombarded with requests from enterprises. These authorities face a great deal of difficulties in protecting enterprises' rights such as copyright, license, or uniqueness of logo or trademark since they have only conventional clustering. Urgent and essential need for sufficient automatic trademark image retrieval system, therefore, is entirely worth thorough research. In this paper, we propose a novel trademark image retrieval method in which the input trademark image is first separated into dominant visual shape images then a feature vector for each shape image which is scale-, rotation-, and translation-invariant is created. Finally, a similarity measure between two trademarks is calculated based on these feature vectors. Given a query trademark image, retrieval procedure is carried out by taking the most five similar trademark images in a predefined trademark. Various experiments are conducted to mimic the many types of trademark copying.

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