Expression based image retrieval using RSCM

S. Stalin, R. Jebi
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Abstract

In recent years, image retrieval has much impact on handhold mobile applications to high-end database processing. Text and content based image retrieval are the common methods in existence. Though it made lot of changes, retrieving human expressions is challenging one for the recent researchers. We introducing a new technique called Ratio of Sum to Centre and Maximum pixel (RSCM) feature which leads to communicate with machines via facial expressions. In the proposed system, first the images in the database get pre-processed and this image can be given to the gabor filter to segment the image as the gabor wavelet blocks. The output of the gabor filter given to the RSCM feature extraction algorithm to extract various expression based features from the image and Artificial Neural Network (ANN) will train the features. Eventually, the comparative analysis performed using the commonly used methods namely precision and recall were clearly shown that the proposed system provide better results than the conventional systems and retrieve the images based upon the expression.
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基于表达式的RSCM图像检索
近年来,图像检索对手持移动应用的高端数据库处理产生了很大的影响。基于文本和基于内容的图像检索是现有的常用方法。虽然它做了很多改变,但对最近的研究人员来说,检索人类的表情是一个挑战。我们引入了一种新的技术,称为和中心和最大像素比(RSCM)特征,通过面部表情与机器进行交流。在该系统中,首先对数据库中的图像进行预处理,然后将图像交给gabor滤波器作为gabor小波块进行分割。gabor滤波器的输出给RSCM特征提取算法,从图像中提取各种基于表情的特征,人工神经网络(ANN)训练这些特征。最后,通过对常用的精确率和召回率方法进行对比分析,结果表明本文提出的系统比传统的系统提供了更好的结果,并且基于表达式检索图像。
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