34,255 research outputs found
Improved texture image classification through the use of a corrosion-inspired cellular automaton
In this paper, the problem of classifying synthetic and natural texture
images is addressed. To tackle this problem, an innovative method is proposed
that combines concepts from corrosion modeling and cellular automata to
generate a texture descriptor. The core processes of metal (pitting) corrosion
are identified and applied to texture images by incorporating the basic
mechanisms of corrosion in the transition function of the cellular automaton.
The surface morphology of the image is analyzed before and during the
application of the transition function of the cellular automaton. In each
iteration the cumulative mass of corroded product is obtained to construct each
of the attributes of the texture descriptor. In a final step, this texture
descriptor is used for image classification by applying Linear Discriminant
Analysis. The method was tested on the well-known Brodatz and Vistex databases.
In addition, in order to verify the robustness of the method, its invariance to
noise and rotation were tested. To that end, different variants of the original
two databases were obtained through addition of noise to and rotation of the
images. The results showed that the method is effective for texture
classification according to the high success rates obtained in all cases. This
indicates the potential of employing methods inspired on natural phenomena in
other fields.Comment: 13 pages, 14 figure
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
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
Relating visual and semantic image descriptors
This paper addresses the automatic analysis of visual content and extraction of metadata beyond pure visual descriptors. Two approaches are described: Automatic Image Annotation (AIA) and Confidence Clustering (CC). AIA attempts to automatically classify images based on two binary classifiers and is
designed for the consumer electronics domain. Contrastingly, the CC approach does not attempt to assign a unique label to images but rather to organise the database based on concepts
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