Texture Recognition Using Gabor Filter for Extracting Feature Vectors With the Regression Mining Algorithm

Texture Recognition Using Gabor Filter for Extracting Feature Vectors With the Regression Mining Algorithm

Neeraj Bhargava (Maharshi Dayanand Saraswati University, India), Ritu Bhargava (Sofia College, Ajmer, India), Pramod Singh Rathore (ACERC, Ajmer, India) and Abhishek Kumar (Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India)
Copyright: © 2020 |Pages: 14
DOI: 10.4018/IJRCM.2020070103


This article considered only natural types of texture and then applying the Gabor filter for better classifications. The concept used is to discard the stochastic features to avoid any mixing of feature vector while it is extracting from the image dataset. The proposed approach has considered the Gabor filter for texture recognition primarily but with the combined method of spatial width and orientation to get the optimal alignment, this optical alignment mine the maximum feature vector by applying the REP algorithm over the data mined from the texture. This will result in better accuracy in the results. Initially, the frequency response over the surface due to applying Gabor filter has been calculated and then the work proceeded in a manner that first natural images are loaded into the MATLAB tool then it is preprocessed, and then final classifications are performed for final results. The primarily concentrated over texture information of image datasets rather than the multispectral information along with REP regression algorithm to do actual mining of feature vectors. Unlike the conventional approach of the Gabor filter, this article focuses on the variance and spatial relationship between two or more than two pixels. The deviation calculated is used for normalizing the feature vectors, and the accuracy can be hence increase using the proposed commuted technique.
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Digital picture preparing manages the control of advanced pictures through a computer. It is a subfield of signals and frameworks however focuses on the images or pictures. DIP basically works on developing a computer system which can build on an image. The contribution of that framework is an advanced picture and the framework procedure that picture utilizing effective calculations and gives a picture as an output (Figure 1).

Figure 1.

Key stages in digital image processing


The most widely recognized illustration is Adobe Photoshop. It is one of the broadly utilized applications for handling advanced pictures (Abadi et al., 2016). An impression made on a surface at the tip of man's finger, ready to be utilized for recognizing people from the unique pattern lines on the fingers. Fingerprints have been utilizing for over a century. It can be utilized as a part of scientific science to help criminal examinations, biometric frameworks, for example, private and business recognizable proof gadgets for individual ID. It is a standout amongst the most critical biometric advancements which have drawn a generous measure of consideration as of late. A unique mark is contained edges and valleys (Figure 2). The edges are the dim zone of the unique mark, and the valleys are the white region that exists between the edges. The unique mark of an individual is interesting and stays unaltered for over a lifetime. The uniqueness of a finger impression is only controlled by the neighborhood edge (Contan et al., 2015)]

Figure 2.

Ridges and valleys


The unique finger impression acknowledgment is a procedure of deciding if two arrangements of unique finger impression edge detail are from a similar individual. Various methodologies are utilized as a part of a wide range of courses for unique mark acknowledgment which are particulars, connection, edge design. These sorts of strategies can be extensively arranged as details based or surface based.

Fingerprinting is the most reasonable, and in the meantime, the most ideal route for security and are incorporated into some imperative records, for example, individual recognizable proof, individual records, claim Motor personality, and others to keep away from the utilization or access of unapproved people. The qualities of our fingers are special. Every individual has extraordinary and unique edges on the finger. The examples of the unique mark are widely changed, however the exactness of edges stayed unaltered (Jeong et al., 2015) (Figure 3).

Figure 3.

Fingerprint device


Gabor Filter

Gabor channel is a direct channel utilized for edge recognition in picture handling. Recurrence and introduction portrayals of Gabor channels are like those of the human neural – visual system, and it is additionally a fitting model for surface portrayal.

At the point when a Gabor channel is connected to a picture, it gives the most noteworthy reaction at edges and at focuses where surface changes. The accompanying pictures demonstrate a test picture and its change after the channel is connected (Makino & Kaneda, 2018) (Figure 4).

Figure 4.

(a) Input image (b) Gabor filter enhancement


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