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Improving Writer Identification Through Writer Selection

机译:通过作家选择提高作家身份

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In this work we present a method for selecting instances for a writer identification system underpinned on the dissimilarity representation and a holistic representation based on texture. The proposed method is based on a genetic algorithm that surpasses the limitations imposed by large training sets by selecting writers instead of instances. To show the efficiency of the proposed method, we have performed experiments on three different databases (BFL, IAM, and Firemaker) where we can observe not only a reduction of about 50% in the number of writers necessary to build the dissimilarity model but also a gain in terms of identification rate. Comparing the writer selection with the traditional instance selection, we could observe that both strategies produce similar results but the former converges about three times faster.
机译:在这项工作中,我们提出了一种基于相似性表示和基于纹理的整体表示为作者识别系统选择实例的方法。所提出的方法基于一种遗传算法,该遗传算法通过选择作者而不是实例来克服大型训练集所施加的限制。为了证明所提出方法的有效性,我们在三个不同的数据库(BFL,IAM和Firemaker)上进行了实验,我们不仅可以观察到建立相似模型所需的作者数量减少了约50%,而且还发现识别率方面的提高。将作者选择与传统实例选择进行比较,我们可以观察到两种策略都产生相似的结果,但是前者的收敛速度大约快三倍。

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