scholarly journals T1K+: A Database for Benchmarking Color Texture Classification and Retrieval Methods

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1010
Author(s):  
Claudio Cusano ◽  
Paolo Napoletano ◽  
Raimondo Schettini
Keyword(s):  
Design Of Experiments ◽  
Texture Classification ◽  
Construction Materials ◽  
Heterogeneous Database ◽  
High Quality ◽  
Visual Descriptors ◽  
Classification Tasks ◽  
Color Texture

In this paper we present T1K+, a very large, heterogeneous database of high-quality texture images acquired under variable conditions. T1K+ contains 1129 classes of textures ranging from natural subjects to food, textile samples, construction materials, etc. T1K+ allows the design of experiments especially aimed at understanding the specific issues related to texture classification and retrieval. To help the exploration of the database, all the 1129 classes are hierarchically organized in 5 thematic categories and 266 sub-categories. To complete our study, we present an evaluation of hand-crafted and learned visual descriptors in supervised texture classification tasks.

Color Texture Classification Using Shortest Paths in Graphs

2014 ◽  
Vol 23 (9) ◽  
pp. 3751-3761 ◽  
Author(s):  
Jarbas Joaci de Mesquita Sa Junior ◽  
Paulo Cesar Cortez ◽  
Andre Ricardo Backes
Keyword(s):  
Texture Classification ◽  
Shortest Paths ◽  
Color Texture

scholarly journals Unsupervised Local Binary Pattern Histogram Selection Scores for Color Texture Classification

2018 ◽  
Vol 4 (10) ◽  
pp. 112 ◽  
Author(s):  
Mariam Kalakech ◽  
Alice Porebski ◽  
Nicolas Vandenbroucke ◽  
Denis Hamad
Keyword(s):  
Local Binary Pattern ◽  
Color Image ◽  
Texture Classification ◽  
Classification Problems ◽  
Class Label ◽  
Laplacian Score ◽  
Pattern Histogram ◽  
Color Texture

These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a color image. In this paper, two new scores are proposed to select histograms: The adapted Variance score and the adapted Laplacian score. These new scores are computed without considering the class label of the images, contrary to what is done until now. Experiments, achieved on OuTex, USPTex, and BarkTex sets, show that these unsupervised scores give as good results as the supervised ones for LBP histogram selection.

scholarly journals Adaptive Matrices and Filters for Color Texture Classification

2012 ◽  
Vol 47 (1-2) ◽  
pp. 79-92 ◽  
Author(s):  
Ioannis Giotis ◽  
Kerstin Bunte ◽  
Nicolai Petkov ◽  
Michael Biehl
Keyword(s):  
Texture Classification ◽  
Color Texture

scholarly journals Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

2019 ◽  
Vol 9 (15) ◽  
pp. 3130 ◽  
Author(s):  
Navarro ◽  
Perez
Keyword(s):  
Feature Extraction ◽  
Pattern Classification ◽  
Texture Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Post Processing ◽  
Image Partition ◽  
Global And Local ◽  
Bagging Ensemble ◽  
Color Texture

Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color–texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.

scholarly journals Hierarchical and Unsupervised Graph Representation Learning with Loukas’s Coarsening

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 206
Author(s):  
Louis Béthune ◽  
Yacouba Kaloga ◽  
Pierre Borgnat ◽  
Aurélien Garivier ◽  
Amaury Habrard
Keyword(s):  
State Of The Art ◽  
Back Propagation ◽  
Representation Learning ◽  
Graph Representation ◽  
High Quality ◽  
Attributed Graphs ◽  
Information Maximization ◽  
Classification Tasks ◽  
Micro Structures ◽  
Mutual Information Maximization

We propose a novel algorithm for unsupervised graph representation learning with attributed graphs. It combines three advantages addressing some current limitations of the literature: (i) The model is inductive: it can embed new graphs without re-training in the presence of new data; (ii) The method takes into account both micro-structures and macro-structures by looking at the attributed graphs at different scales; (iii) The model is end-to-end differentiable: it is a building block that can be plugged into deep learning pipelines and allows for back-propagation. We show that combining a coarsening method having strong theoretical guarantees with mutual information maximization suffices to produce high quality embeddings. We evaluate them on classification tasks with common benchmarks of the literature. We show that our algorithm is competitive with state of the art among unsupervised graph representation learning methods.

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