Comparison of feature selection schemes for color texture classification

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.

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Color Texture Classification Using Shortest Paths in Graphs

IEEE Transactions on Image Processing ◽

10.1109/tip.2014.2333655 ◽

2014 ◽

Vol 23 (9) ◽

pp. 3751-3761 ◽

Cited By ~ 31

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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LBP histogram selection for supervised color texture classification

2013 IEEE International Conference on Image Processing ◽

10.1109/icip.2013.6738667 ◽

2013 ◽

Cited By ~ 13

Author(s):

A. Porebski ◽

N. Vandenbroucke ◽

D. Hamad

Keyword(s):

Texture Classification ◽

Selection For ◽

Color Texture

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Unsupervised Local Binary Pattern Histogram Selection Scores for Color Texture Classification

Journal of Imaging ◽

10.3390/jimaging4100112 ◽

2018 ◽

Vol 4 (10) ◽

pp. 112 ◽

Cited By ~ 4

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.

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