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Analysis of SVM kernels for content based image retrieval system

机译：基于内容的图像检索系统的SVM内核分析

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The Content Based Image Retrieval (CBIR) system uses knowledge of computer vision for the research. Nowadays, a lot of images are being generated due to extensive use of smart phone and being spreaded worldwide due to the popularity of social media and high speed internet services. To remove the language ambiguity problem, images are required sophisticated analysis instead of simple textual base analysis and hence CBIR is developed. It is a new way to retrieve images based on color, texture, and shape features of the images. The major challenges of the CBIR systems are the retrieval accuracy and the computational complexity. In this paper, color moments are used due to its small feature vector which will reduce computational complexity. Gray Level Co-Occurrence Matrix is used as texture feature to extract a repetitive pattern of the image. Support Vector Machine (SVM) is used as a classifier and it will remove irrelevant images and hence improve retrieval accuracy. The linear and non-linear SVM classifier is used to predict category of the query images and filter out the irrelevant images. In non-linear SVM classifier, three different kernels: Polynomial, Radial Bases Function (RBF) and Sigmoidal function are used. It is proved that RBF performs good due to its exponential kernel and hence resolve the problem in infinite dimensions. The results of CBIR are compared for linear and nonlinear SVM classifier and also for different fusion techniques in different color space using average precision rate.
机译：基于内容的图像检索（CBIR）系统使用计算机视觉知识进行研究。如今，由于智能手机的广泛使用，正在生成大量图像，由于社交媒体和高速互联网服务的普及，图像已在全球范围内传播。为了消除语言歧义性问题，需要对图像进行复杂的分析，而不是简单的文本基础分析，因此开发了CBIR。这是一种基于图像的颜色，纹理和形状特征检索图像的新方法。 CBIR系统的主要挑战是检索准确性和计算复杂性。在本文中，由于使用了色彩矩，因为它的特征向量较小，因此可以降低计算复杂度。灰度共生矩阵用作纹理特征，以提取图像的重复图案。支持向量机（SVM）被用作分类器，它将去除不相关的图像，从而提高检索精度。线性和非线性SVM分类器用于预测查询图像的类别并过滤掉不相关的图像。在非线性SVM分类器中，使用了三个不同的内核：多项式，径向基函数（RBF）和Sigmoidal函数。事实证明，RBF的指数核性能很好，因此可以解决无限维问题。使用平均准确率比较线性和非线性SVM分类器的CBIR结果，以及不同颜色空间中不同融合技术的CBIR结果。

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