143 research outputs found
Multi-resolution texture classification based on local image orientation
The aim of this paper is to evaluate quantitatively the discriminative power of the image orientation in the texture classification process. In this regard, we have evaluated the performance of two texture classification schemes where the image orientation is extracted using the partial derivatives of the Gaussian function. Since the texture descriptors are dependent on the observation scale, in this study the main emphasis is placed on the implementation of multi-resolution texture analysis schemes. The experimental results were obtained when the analysed texture descriptors were applied to standard texture databases
Enhancing Volumetric Bouligand-Minkowski Fractal Descriptors by using Functional Data Analysis
This work proposes and study the concept of Functional Data Analysis
transform, applying it to the performance improving of volumetric
Bouligand-Minkowski fractal descriptors. The proposed transform consists
essentially in changing the descriptors originally defined in the space of the
calculus of fractal dimension into the space of coefficients used in the
functional data representation of these descriptors. The transformed decriptors
are used here in texture classification problems. The enhancement provided by
the FDA transform is measured by comparing the transformed to the original
descriptors in terms of the correctness rate in the classification of well
known datasets
Experimentation on the Use of Chromaticity Features, Local Binary Pattern, and Discrete Cosine Transform in Colour Texture Analysis
Texture segmentation by minimizing vector-valued energy functionals: The Coupled-Membrane model
ACCURATE IMAGE RETRIEVAL BASED ON COMPACT COMPOSITE DESCRIPTORS AND RELEVANCE FEEDBACK INFORMATION
Predicting complexity perception of real world images
The aim of this work is to predict the complexity perception of real world images.We propose a new complexity measure where different image features, based on spatial, frequency and color properties are linearly combined. In order to find the optimal set of weighting coefficients we have applied a Particle Swarm Optimization. The optimal linear combination is the one that best fits the subjective data obtained in an experiment where observers evaluate the complexity of real world scenes on a web-based interface. To test the proposed complexity measure we have performed a second experiment on a different database of real world scenes, where the linear combination previously obtained is correlated with the new subjective data. Our complexity measure outperforms not only each single visual feature but also two visual clutter measures frequently used in the literature to predict image complexity. To analyze the usefulness of our proposal, we have also considered two different sets of stimuli composed of real texture images. Tuning the parameters of our measure for this kind of stimuli, we have obtained a linear combination that still outperforms the single measures. In conclusion our measure, properly tuned, can predict complexity perception of different kind of images
Comparison of various texture classification methods using multiresolution analysis and linear regression modelling
Uma avaliação de descritores de textura baseados em códigos binários locais para classificação de imagens de sensoriamento remoto
Co-occurrence context of the data-driven quantized local ternary patterns for visual recognition
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