Abstract
A novel unsupervised texture classification technique is proposed in this research work. The proposed method clusters automatically the textures of an image collection in similarity classes whose number is not a priori known. A nonlinear diffusion-based multi-scale texture analysis approach is introduced first. It creates an effective scale-space by using a well-posed anisotropic diffusion filtering model that is proposed and approximated numerically here. A feature extraction process using a bank of circularly symmetric 2D filters is applied at each scale, then a rotation-invariant texture feature vector is achieved for the current image by combining the feature vectors computed at all these scales. Next, a weighted similarity graph, whose vertices correspond to the texture feature vectors and the weights of its edges are obtained from the distances computed between these vectors, is created. A novel weighted graph clustering technique is then applied to this similarity graph, to determine the texture classes. Numerical simulations and method comparisons illustrating the effectiveness of the described framework are also discussed in this work.
Highlights
The image textures represent sets of primitives, known as texels, in some regular or repeated patterns
The novel automatic unsupervised rotation-invariant texture recognition technique introduced in this work brings together several important research domains, such as image processing and analysis, partial differential equations, numerical analysis and graph theory
The feature extraction component of the recognition framework is based on a new multi-scale texture analysis approach that is an important contribution of our research
Summary
The image textures represent sets of primitives, known as texels, in some regular or repeated patterns. Texture analysis is an important and still challenging image analysis field that include various sub-domains, such as texture recognition, segmentation, synthesis and retrieval. It has been applied in some well-known image processing and computer vision domains, such as image and video object detection, recognition and tracking, image and video indexing and retrieval, medical imaging, remote sensing and product quality diagnosis. A texture recognition process consists of texture feature extraction and texture categories classification. The texture feature extraction methods could be divided into statisticsbased, structure-based, model-based and transformation-based schemes. The statistical techniques include histogram-based approaches [1], moment-based algorithms [2], Gray
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