基于内容的增强Gabor小波变换图像检索

Anusha Yalavarthi, K. Veeraswamy, K. Sheela
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引用次数: 10

摘要

目前,基于内容的图像检索(CBIR)是检索颜色、形状和纹理的最强大、最流行的方法。为了提高检索效率,本文提出了一种基于内容的图像检索方法——增强Gabor小波变换。Gabor小波变换(GWT)被广泛关注于将平面波的特征与Gabor函数结合形成非正交函数。利用GWT将训练库图像的挑战属性分解为不同的尺度和方向,并使用不同的过滤器来减少图像的不需要信息。实验结果表明,Gabor小波变换在检索效率和计算率方面都优于现有的方法。
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Content based image retrieval using enhanced Gabor wavelet transform
Nowadays content-based image retrieval (CBIR) is the most powerful and popular method for retrieving color, shape, and texture. In this paper, we proposed content based image retrieval using enhanced Gabor wavelet transform for increasing the retrieval efficiency. Gabor wavelet transform (GWT) is widely concentrated on the combination of features of plane wave and Gabor function to form non-orthogonal functions. The challenge property of the training database images using GWT is decomposed into different scaling and orientation with different filters to reduce the unwanted information of the images. The proposed experimental results of the Gabor wavelet transform give excellent results in terms of retrieval efficiency also computational rates as compared to existing techniques.
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