Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition

Kazak, Nihan

doi:10.18100/ijamec.270683

Cited by 7 publications

(3 citation statements)

References 14 publications

(17 reference statements)

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“…Fig. 2, shows examples of the resulting embedding for the Describable Textures Dataset (DTD) [CMK * 14] and the UIUC Texture Database [Kaz16]. t-SNE has the advantage of being non-linear and preserves neighborhood relations, which is particularly desirable for exploration purposes.…”

Section: Embeddingmentioning

confidence: 99%

Texture Browser: Feature‐based Texture Exploration

Luo

Scandolo

Eisemann

2021

Computer Graphics Forum

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Texture is a key characteristic in the definition of the physical appearance of an object and a crucial element in the creation process of 3D artists. However, retrieving a texture that matches an intended look from an image collection is difficult. Contrary to most photo collections, for which object recognition has proven quite useful, syntactic descriptions of texture characteristics is not straightforward, and even creating appropriate metadata is a very difficult task. In this paper, we propose a system to help explore large unlabeled collections of texture images. The key insight is that spatially grouping textures sharing similar features can simplify navigation. Our system uses a pre‐trained convolutional neural network to extract high‐level semantic image features, which are then mapped to a 2‐dimensional location using an adaptation of t‐SNE, a dimensionality‐reduction technique. We describe an interface to visualize and explore the resulting distribution and provide a series of enhanced navigation tools, our prioritized t‐SNE, scalable clustering, and multi‐resolution embedding, to further facilitate exploration and retrieval tasks. Finally, we also present the results of a user evaluation that demonstrates the effectiveness of our solution.

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Section: Embeddingmentioning

confidence: 99%

Texture Browser: Feature‐based Texture Exploration

Luo

Scandolo

Eisemann

2021

Computer Graphics Forum

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“…Since a circular neighborhood with unit radius is used, the intensity values of the equally spaced sampling points are calculated using bilinear interpolation when they are not in the center of a pixel. LBP and its variants are used several machine vision problems such as, face recognition [6,15], texture recognition [16][17][18][19][20], object recognition [21], etc. Shape is one of the most basic geometric features that is used to describe the content of the image.…”

Section: Related Workmentioning

confidence: 99%

Histogram of Edge Segment Curvatures for Texture Recognition

Koç¹,

Topal²

2018

Anadolu University Journal of Science and Technology-a Applied Sciences and Engineering

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Texture recognition is one of the active fields in pattern recognition. Researchers have been searching for the best representation of a texture image for decades. The majority of methods use appearance-based properties of texture images to generate a feature descriptor. In this paper, we propose novel feature descriptor, namely histogram of edge segment curvatures (HESC) which extracts edge segments of an input image and construct a histogram from quantized curvature values of them. Therefore, HESC unveils geometric information of texture images by utilizing curve strengths for each pixel along the edge segments. We show that the proposed feature descriptor is robust against rotation and translation. We also extend HESC descriptor to emphasis the contribution of small curvature values. We carry out several experiments in UIUC texture dataset and compare the performance of the proposed HESC descriptor to well-known Local Binary Pattern (LBP). The proposed texture descriptor outperforms LBP in terms of recognition accuracy.

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“…The aim of image processing is to manipulate pixels to obtain the best clarity in the images and to extract target zones by using an input image (Uchida, 2013). Local binary pattern (LBP) is one of the strongest and most popular tools for obtaining local image patterns (Kazak & Koç, 2016;Muthevi & Uppu, 2017;Tuncer and Aydemir, 2020). LBP, proposed by Ojala et al (2000Ojala et al ( , 2002, is characterized as a gray scale texture developed from the main texture in local neighborhood pixels (Kaya et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Different Approach To The Chromosome Classification of Three Lallemantia Fisch. & C.A. Mey. Species By Using Local Binary Pattern (LBP)

Emre

Tuncer

Doğan

et al. 2022

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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The traditional karyotype studies are widely used in plant systematics to evaluate the positions of species. However, studies sometimes can not solve systematic problems. In recent years, computer-based systems have gained importance in contributing to the solution of the taxonomical problems. The aim of this study was to identify three Lallemantia species by using the LBP (local binary pattern) texture operator based on chromosome images in mitosis. LBP is the one of the most powerful and easily applicable tool for identifying local image patterns. In this study, microphotographs of 641 cells in the metaphase stage of mitosis were used. The LBP involves preprocessing, feature extraction, feature selection, and classification. Decision tree (DT), linear discriminant (LD), support vector machine (SVM), K-nearest neighbor (KNN), bagged tree (BT) and ensemble subspace nearest neighbor (SNN) were used in the classification stage. This study found that the best acting classifier was SNN because achievement rate was one hundred percent. Also, a dendrogram was formed to measure the similarity among the three species. As a result, LBP can be accepted as a tool for classifying plants by using chromosome images.

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Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition

Cited by 7 publications

References 14 publications

Texture Browser: Feature‐based Texture Exploration

Texture Browser: Feature‐based Texture Exploration

Histogram of Edge Segment Curvatures for Texture Recognition

A Different Approach To The Chromosome Classification of Three Lallemantia Fisch. & C.A. Mey. Species By Using Local Binary Pattern (LBP)

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