Gabor wavelets combined with volumetric fractal dimension applied to texture analysis

Zuñiga, Alvaro G.; Florindo, João Batista; Bruno, Odemir Martinez

doi:10.1016/j.patrec.2013.09.023

Cited by 50 publications

(17 citation statements)

References 28 publications

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“…It is an excellent feature representation that is not affected by the illumination or expression variation. In literature, there are a lot of Gabor Extraction methods which give very outstanding results with good performance indicator and ability to work with wide range of applications such as local normalization entropy-like weighted Gabor features [10], Gabor wavelets combined with volumetric fractal dimension [11] and fusion of multi-channels classifier [12]. Neural networks have been extensively used for purpose of objects classification.…”

Section: Related Workmentioning

confidence: 99%

A Feature Fusion Approach for Hand Tools Classification

Ibrahim¹,

Ahmed²

2017

ijacsa

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Abstract-The most important functions in objects classification and recognition system are to segment the objects from the input image, extract common features from the objects, and classify these objects as a member of one of the considered object classes. In this paper, we present a new approach for feature-based objects classification. The main idea of the new approach is the fusion of two different feature vectors that are calculated using Fourier descriptors and moment invariants. The fused moment-Fourier feature vector is invariant to image scaling, rotation, and translation. The fused feature vector for a reference object is used for training feed-forward neural network classifier. Classification of some hand tools is used to evaluate the performance of the proposed classification approach. The results show an appreciable increase in the classification accuracy rate with a considerable decrease in the classifier learning time.

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Section: Related Workmentioning

confidence: 99%

A Feature Fusion Approach for Hand Tools Classification

Ibrahim¹,

Ahmed²

2017

ijacsa

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“…It is generally believed that the extraction of powerful texture features is more important than that of weak texture features since they do not lead to good classification results, even when using excellent classifiers [8]. However, texture features can be found in various orientations and at different scales, and these cannot be characterized effectively by commonly used methods [9][10][11][12]. Gabor filter has been used for this purpose and performs better in the discrimination of individual texture features, especially those with similar descriptions.…”

Section: Introductionmentioning

confidence: 99%

A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer

et al. 2019

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Texture classification is an important topic for many applications in machine vision and image analysis, and Gabor filter is considered one of the most efficient tools for analyzing texture features at multiple orientations and scales. However, the parameter settings of each filter are crucial for obtaining accurate results, and they may not be adaptable to different kinds of texture features. Moreover, there is redundant information included in the process of texture feature extraction that contributes little to the classification. In this paper, a new texture classification technique is detailed. The approach is based on the integrated optimization of the parameters and features of Gabor filter, and obtaining satisfactory parameters and the best feature subset is viewed as a combinatorial optimization problem that can be solved by maximizing the objective function using hybrid ant lion optimizer (HALO). Experimental results, particularly fitness values, demonstrate that HALO is more effective than the other algorithms discussed in this paper, and the optimal parameters and features of Gabor filter are balanced between efficiency and accuracy. The method is feasible, reasonable, and can be utilized for practical applications of texture classification.

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“…The developed methods have been successfully used in a number of different fields of application, such as Medicine [2], Biology [3], Engineering [4], etc. Several approaches have been described in the literature to extract meaningful information from texture images [1] and a particular category of these methods comprise those employing some sort of image transform, like Fourier [5], wavelets [6], and Gabor [7], for example. Most of these transforms were primarily developed for image processing, and hence they have the property of describing the image from another viewpoint, clarifying patterns that were not evident by the simple inspection of pixel values.…”

Section: Introductionmentioning

confidence: 99%

Discrete Schroedinger transform for texture recognition

Florindo

Bruno

2017

Information Sciences

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This work presents a new procedure to extract features of grey-level texture images based on the discrete Schroedinger transform. This is a non-linear transform where the image is mapped as the initial probability distribution of a wave function and such distribution evolves in time following the Schroedinger equation from Quantum Mechanics. The features are provided by statistical moments of the distribution measured at different times. The proposed method is applied to the classification of three databases of textures used for benchmark and compared to other well-known texture descriptors in the literature, such as textons, local binary patterns, multifractals, among others. All of them are outperformed by the proposed method in terms of percentage of images correctly classified. The proposal is also applied to the identification of plant species using scanned images of leaves and again it outperforms other texture methods. A test with images affected by Gaussian and "salt & pepper" noise is also carried out, also with the best performance achieved by the Schroedinger descriptors.

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Gabor wavelets combined with volumetric fractal dimension applied to texture analysis

Cited by 50 publications

References 28 publications

A Feature Fusion Approach for Hand Tools Classification

A Feature Fusion Approach for Hand Tools Classification

A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer

Discrete Schroedinger transform for texture recognition

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