International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
www.irjet.net
A Survey on Feature Descriptors for Texture Image Classification Anu S Nair1, Ritty Jacob2 1PG
student, Dept. of Computer Engineering, VJCET, Vazhakulam, Kerala, India
2Assistant
Professor, Dept. of Computer Engineering, VJCET, Vazhakulam, Kerala, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract – Texture is a fundamental feature which
Texture classification consists of mainly two steps: feature extraction and classifier designation. A successful classification requires an efficient description of image texture. A variety of new feature descriptors for texture classification have been proposed in last few decades. An effective descriptor has to solve the problems such as rotation, illumination change, scale, blur, noise and occlusion. In order to achieve classification accuracy, the quality of the descriptor and its computational complexity must be balanced. Much work has been done on creating advanced feature descriptors to improve the texture classification accuracy. This paper attempts to study various feature descriptor generation methods for effectively classifying the images based on their texture.
Key Words: Texture image, local binary pattern, classification accuracy, local features, feature extraction.
2. LITERATURE SURVEY
describes the appearance of the surface of some entity. Nowadays texture based image classification methods have an important role in various computer vision problems. Many descriptors can be used to perform texture classification. The descriptors will identify and describe the textural features of an image. Texture classification system encounters the problems such as the scale variations, illumination changes, and rotation. This paper attempts in exploring the survey of various image classification techniques using different textural feature descriptors. All these approaches have used an appropriate classifier to classify the textural features. The effective combination of descriptive image features and the selection of a suitable classification method are very much significant for improving classification accuracy.
1. INTRODUCTION Nowadays due to the rapid development in technology and high availability of computing facilities, tremendous amount of data is generated. As a result, there has been a huge increase in the amount of image data on the Web. The accumulation of large collections of these digital images has created the need for intelligent and efficient schemes for image classification. The image classification techniques must maximize its performance for intelligent decision making. In order to classify images, initially we want to find out the basic type of features which describe the visual properties of that image such as its color, texture, shape, gradient, etc. Texture is an important feature of objects in an image. Texture is actually a visual characteristic which describing the appearance of the surface of some object. Most objects have their own distinct texture, such as the surface of earth, tree trunk, sky, and bunch of flowers etc. Texture classification is an active research area in number of computer vision problems such as object detection, material classification, fabric inspection, face recognition, contentbased image retrieval, medical image analysis, facial expression recognition, image segmentation, biometrics and remote sensing. Therefore, the fundamental problems associated with texture classification are highly relevant. Š 2017, IRJET
|
Impact Factor value: 5.181
|
In [1] Timo Ojala et al. proposed a simple and efficient descriptor called local binary patterns for gray-scale and rotation invariant texture classification. This descriptor derives an operator which is invariant against any monotonic transformation of the gray scale. This paper describes that, the fundamental properties of local image texture are certain local binary texture patterns termed as "uniform". In order to detecting these "uniform" patterns, a generalized rotation invariant and gray-scale operator has been developed. The term "uniform" indicates that the uniform appearance of the local binary. The name of the descriptor reflects the functionality of the operator, i.e., the local neighborhood values are threshold at the gray value of the center pixel into a binary code pattern. The spatial configuration of local image texture is characterized by these operators. The performance can be improved by combining them with rotation invariant variance measures that characterize the contrast of local image texture. In [2] Liao et al. proposed a new feature extraction method that is robust to histogram equalization and rotation. In order to effectively capture the dominating patterns in texture images, this method extended the conventional LBP approach into the dominant local binary pattern (DLBP). Unlike the conventional LBP approach which exploits only the uniform LBP, DLBP approach computes the occurrence frequencies of all rotation invariant patterns. These patterns are then sorted in descending order. The first several most ISO 9001:2008 Certified Journal
|
Page 782