A novel method for image categorization based on histogram oriented gradient and support vector machine

Mohammed Reda Guedira, A. E. Qadi, Mohammed Rziza Lrit, M. Hassouni
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引用次数: 7

Abstract

In this paper, we introduce a new method for categorisation natural image based on different techniques. Concerning the color and texture, we made a pre-treatment to convert the database images on to the gray-scale and the Haar wavelet transformation. For this, we use the Oriented Gradient Histogram (HOG) for each sub-band to extract these image features. We have used a proper classification based on the support vector machine (SVM) to recognize these images. The result part and experience applied on a Corel database that is known in natural images shows a better performance of the proposed system based on accuracy and speed compared to other CBIR methods.
基于直方图梯度和支持向量机的图像分类新方法
本文介绍了一种基于不同技术的自然图像分类新方法。在颜色和纹理方面,对数据库图像进行预处理,将其转换为灰度图像,并进行Haar小波变换。为此,我们对每个子带使用定向梯度直方图(HOG)来提取这些图像特征。我们使用了基于支持向量机(SVM)的适当分类来识别这些图像。结果部分和在自然图像中已知的Corel数据库上的应用经验表明,与其他CBIR方法相比,该系统在精度和速度方面具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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