Abstract book (pdf) - ICPR 2010

TuAT6 Dolmabahçe Hall B
Texture Regular Session
Session chair: Theodoridis, Sergios (Univ. of Athens)
09:00-09:20, Paper TuAT6.1
On Adapting Pixel-Based Classification to Unsupervised Texture Segmentation
Melendez, Jaime, Rovira I Virgili Univ.
Puig, Domenec, Univ. Rovira I Virgili
Garcia, Miguel Angel, Autonomous Univ. of Madrid
An inherent problem of unsupervised texture segmentation is the absence of previous knowledge regarding the texture
patterns present in the images to be segmented. A new efficient methodology for unsupervised image segmentation based
on texture is proposed. It takes advantage of a supervised pixel-based texture classifier trained with feature vectors associated
with a set of texture patterns initially extracted through a clustering algorithm. Therefore, the final segmentation is
achieved by classifying each image pixel into one of the patterns obtained after the previous clustering process. Multisized
evaluation windows following a top-down approach are applied during pixel classification in order to improve accuracy.
The proposed technique has been experimentally validated on MeasTex, VisTex and Brodatz compositions, as well
as on complex ground and aerial outdoor images. Comparisons with state-of the-art unsupervised texture segmenters are
also provided.
09:20-09:40, Paper TuAT6.2
Natural Material Recognition with Illumination Invariant Textural Features
Vacha, Pavel, Inst. of Information Theory and Automation
Haindl, Michael, Inst. of Information Theory and Automation
A visual appearance of natural materials fundamentally depends on illumination conditions, which significantly complicates
a real scene analysis. We propose textural features based on fast Markovian statistics, which are simultaneously invariant
to illumination colour and robust to illumination direction. No knowledge of illumination conditions is required and a
recognition is possible from a single training image per material. Material recognition is tested on the currently most realistic
visual representation – Bidirectional Texture Function (BTF), using the Amsterdam Library of Textures (I), which
contains 250 natural materials acquired in different illumination conditions. Our proposed features significantly outperform
several leading alternatives including Local Binary Patterns (LBP, LBP-HF) and Gabor features.
09:40-10:00, Paper TuAT6.3
Gaze-Motivated Compression of Illumination and View Dependent Textures
Filip, Jiri, Inst. of Information Theory and Automation of the AS CR
Haindl, Michael, Inst. of Information Theory and Automation
Chantler, Michael J., Heriot-Watt Univ.
Illumination and view dependent texture provide ample information on the appearance of real materials at the cost of enormous
data storage requirements. Hence, past research focused mainly on compression and modelling of these data, however,
few papers have explicitly addressed the way in which humans perceive these compressed data. We analyzed human
gaze information to determine appropriate texture statistics. These statistics were then exploited in a pilot illumination
and view direction dependent data compression algorithm. Our results showed that taking into account local texture variance
can increase compression of current methods more than twofold, while preserving original realistic appearance and
allowing fast data reconstruction.
10:00-10:20, Paper TuAT6.4
Perceptual Color Texture Codebooks for Retrieving in Highly Diverse Texture Datasets
Alvarez, Susana, Univ. Rovira I Virgili
Salvetella, Anna, Univ. Autònoma de Barcelona
Vanrell, Maria, Univ. Autònoma de Barcelona
Otazu, Xavier, Univ. Autònoma de Barcelona
Color and texture are visual cues of different nature, their integration in a useful visual descriptor is not an obvious step.
One way to combine both features is to compute texture descriptors independently on each color channel. A second way
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