11,903 research outputs found
Improving Texture Categorization with Biologically Inspired Filtering
Within the domain of texture classification, a lot of effort has been spent
on local descriptors, leading to many powerful algorithms. However,
preprocessing techniques have received much less attention despite their
important potential for improving the overall classification performance. We
address this question by proposing a novel, simple, yet very powerful
biologically-inspired filtering (BF) which simulates the performance of human
retina. In the proposed approach, given a texture image, after applying a DoG
filter to detect the "edges", we first split the filtered image into two "maps"
alongside the sides of its edges. The feature extraction step is then carried
out on the two "maps" instead of the input image. Our algorithm has several
advantages such as simplicity, robustness to illumination and noise, and
discriminative power. Experimental results on three large texture databases
show that with an extremely low computational cost, the proposed method
improves significantly the performance of many texture classification systems,
notably in noisy environments. The source codes of the proposed algorithm can
be downloaded from https://sites.google.com/site/nsonvu/code.Comment: 11 page
Evaluation of local orientation for texture classification
The aim of this paper is to present a study where we evaluate the optimal inclusion of the texture orientation
in the classification process. In this paper the orientation for each pixel in the image is extracted using the
partial derivatives of the Gaussian function and the main focus of our work is centred on the evaluation of
the local dominant orientation (which is calculated by combining the magnitude and local orientation) on
the classification results. While the dominant orientation of the texture depends strongly on the observation
scale, in this paper we propose to evaluate the macro-texture by calculating the distribution of the dominant
orientations for all pixels in the image that sample the texture at micro-level. The experimental results were
conducted on standard texture databases and the results indicate that the dominant orientation calculated at
micro-level is an appropriate measure for texture description
A new Edge Detector Based on Parametric Surface Model: Regression Surface Descriptor
In this paper we present a new methodology for edge detection in digital
images. The first originality of the proposed method is to consider image
content as a parametric surface. Then, an original parametric local model of
this surface representing image content is proposed. The few parameters
involved in the proposed model are shown to be very sensitive to
discontinuities in surface which correspond to edges in image content. This
naturally leads to the design of an efficient edge detector. Moreover, a
thorough analysis of the proposed model also allows us to explain how these
parameters can be used to obtain edge descriptors such as orientations and
curvatures.
In practice, the proposed methodology offers two main advantages. First, it
has high customization possibilities in order to be adjusted to a wide range of
different problems, from coarse to fine scale edge detection. Second, it is
very robust to blurring process and additive noise. Numerical results are
presented to emphasis these properties and to confirm efficiency of the
proposed method through a comparative study with other edge detectors.Comment: 21 pages, 13 figures and 2 table
Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification
We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include the support vector machine, Gaussian process classifiers, random subspace of adaboost, random subspace of rotation boosting, and deep learning classifiers. We demonstrate that a heterogeneous ensemble based on the simple fusion by sum rule of different classifiers performs consistently well across all twenty-five datasets. The most important result of our investigation is demonstrating that some very recent approaches, including the heterogeneous ensemble we propose in this paper, are capable of outperforming an SVM classifier (implemented with LibSVM), even when both kernel selection and SVM parameters are carefully tuned for each dataset
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