Rotation invariant features for color texture classification and retrieval under varying illumination

2011 ◽  
Vol 16 (1) ◽  
pp. 69-81
Author(s):  
B. Sathyabama ◽  
M. Anitha ◽  
S. Raju ◽  
V. Abhaikumar
Keyword(s):  
Texture Classification ◽  
Rotation Invariant ◽  
Invariant Features ◽  
Color Texture

scholarly journals Rotation-invariant features based on directional coding for texture classification

2018 ◽  
Vol 31 (10) ◽  
pp. 6393-6400 ◽  
Author(s):  
Farida Ouslimani ◽  
Achour Ouslimani ◽  
Zohra Ameur
Keyword(s):  
Texture Classification ◽  
Rotation Invariant ◽  
Invariant Features ◽  
Directional Coding

Continuous rotation invariant features for gradient-based texture classification

2015 ◽  
Vol 132 ◽  
pp. 87-101 ◽  
Author(s):  
Kazim Hanbay ◽  
Nuh Alpaslan ◽  
Muhammed Fatih Talu ◽  
Davut Hanbay ◽  
Ali Karci ◽  
...  
Keyword(s):  
Texture Classification ◽  
Rotation Invariant ◽  
Invariant Features ◽  
Continuous Rotation ◽  
Gradient Based

Texture classification using Gabor wavelets based rotation invariant features

2006 ◽  
Vol 27 (16) ◽  
pp. 1976-1982 ◽  
Author(s):  
S. Arivazhagan ◽  
L. Ganesan ◽  
S. Padam Priyal
Keyword(s):  
Texture Classification ◽  
Gabor Wavelets ◽  
Rotation Invariant ◽  
Invariant Features

Noise robust rotation invariant features for texture classification

2013 ◽  
Vol 46 (8) ◽  
pp. 2103-2116 ◽  
Author(s):  
Rouzbeh Maani ◽  
Sanjay Kalra ◽  
Yee-Hong Yang
Keyword(s):  
Texture Classification ◽  
Rotation Invariant ◽  
Invariant Features ◽  
Noise Robust

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
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