Statistical Features for Image Retrieval A Quantitative Comparison

被引：0

作者：

Di Ruberto, Cecilia ^{[1
]}

Fodde, Giuseppe ^{[1
]}

机构：

[1] Univ Cagliari, Dept Math & Comp Sci, Via Osped 72, I-09124 Cagliari, Italy

来源：

PROCEEDINGS OF THE 2014 9TH INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS (VISAPP), VOL 1 | 2014年

关键词：

Texture; Feature Extraction; Feature Selection; Classification; Statistical Texture Analysis; PERCEPTION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we present a comparison between various statistical descriptors and analyze their goodness in classifying textural images. The chosen statistical descriptors have been proposed by Tamura, Battiato and Haralick. In this work we also test a combination of the three descriptors for texture analysis. The databases used in our study are the well-known Brodatz's album and DDSM (Heath et al., 1998). The computed features are classified using the Naive Bayes, the RBF, the KNN, the Random Forest and Random Tree models. The results obtained from this study show that we can achieve a high classification accuracy if the descriptors are used all together.

引用

页码：610 / 617

页数：8

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