Texture classification by wavelet packet signatures (original) (raw)
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Texture classification using wavelet transform
Pattern Recognition Letters, 2003
Today, texture analysis plays an important role in many tasks, ranging from remote sensing to medical imaging and query by content in large image data bases. The main difficulty of texture analysis in the past was the lack of adequate tools to characterize different scales of ...
Wavelet packet neural networks for texture classification
Expert Systems With Applications, 2007
Texture can be defined as a local statistical pattern of texture primitives in observer's domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. This paper describes the usage of wavelet packet neural networks (WPNN) for texture classification problem. The proposed schema composed of a wavelet packet feature extractor and a multi-layer perceptron classifier. Entropy and energy features are integrated wavelet feature extractor. The performed experimental studies show the effectiveness of the WPNN structure. The overall success rate is about 95%.
Wavelet based texture classification
Proceedings 15th International Conference on Pattern Recognition. ICPR-2000
Textures are one of the basic features in visual searching and computational vision. In the literature, most of the attention has been focussed on the texture features with minimal consideration of the noise models. In this paper we investigated the problem of texture classification from a maximum likelihood perspective. We took into account the texture model, the noise distribution, and the interdependence of the texture features. Our investigation showed that the real noise distribution is closer to an Exponential than a Gaussian distribution, and that the L 1 metric has a better retrieval rate than L 2. We also proposed the Cauchy metric as an alternative for both the L 1 and L 2 metrics. Furthermore, we provided a direct method for deriving an optimal distortion measure from the real noise distribution, which experimentally provides consistently improved results over the other metrics. We conclude with results and discussions on an international texture database.
Wavelet Based Features for Texture Classification
2006
This paper presents a feature extraction algorithm using wavelet decomposed images of an image and its complementary image for texture classification. The features are constructed from the different combination of sub-band images. These features offer a better discriminating strategy for texture classification and enhance the classification rate. In our study we have used the Euclidean distance measure and the minimum distance classifier to classify the texture. The experimental results demonstrate the efficiency of the proposed algorithm.
Texture Classification with Feature Analysis Using Wavelet Approach: A Review
2015
Textures play important roles in many image processing applications, since images of real objects often do not exhibit regions of uniform and smooth intensities, but variations of intensities with certain repeated structures or patterns, referred to as visual texture. The textural patterns or structures mainly result from the physical surface properties, such as roughness or oriented structured of a tactile quality. It is widely recognized that a visual texture, which can easily perceive, is very difficult to define. The difficulty results mainly from the fact that different people can define textures in applications dependent ways or with different perceptual motivations, and they are not generally agreed upon single definition of texture. The development in multi-resolution analysis such as Local Binary Pattern and wavelet transform help to overcome this difficulty.
Texture discrimination using multimodal wavelet packet subbands
2004 International Conference on Image Processing, 2004. ICIP '04.
The subband histograms of wavelet packet bases adapted to individual texture classes often fail to display the leptokurtotic behaviour shown by the standard wavelet coefficients of 'natural' images. While many subband histograms remain leptokurtotic in adaptive bases, some subbands are Gaussian. Most interestingly, however, some subbands show multimodal behaviour, with no mode at zero. In this paper, we provide evidence for the existence of these multimodal subbands and show that they correspond to narrow frequency bands running throughout images of the texture. They are thus closely linked to the texture's structure. As such, they seem likely to possess superior descriptive and discriminative power as compared to unimodal subbands. We demonstrate this using both Brodatz and remote sensing images.
Study of the relative magnitude in the wavelet domain for texture characterization
Signal, Image and Video Processing, 2018
Wavelet-based transforms have emerged as efficient directional multiscale schemes able to provide advanced analysis for the textural content of an image. Making use of their statistical dependencies, wavelet coefficients have been recognized as good basis for texture analysis. In this paper, we propose a new feature vector called relative magnitude (RM) which incorporates local statistical dependencies within the neighborhood of magnitude coefficients. Its discriminative power is evaluated on multiclass grayscale texture classification. The generalized Gaussian distribution and the Laplace Model are used to study the statistical behavior of the proposed feature vector. Experiments were conducted on textures from the VisTex, Brodatz, Outex_TC10, UMD, UIUC, and KTH_TIPS databases. Quantitative results demonstrate the efficiency of the RM feature vector for texture discrimination in the wavelet domain. Keywords Relative magnitude • Directional wavelet-based transforms • Texture • Classification 1 Introduction Texture analysis is a fascinating problem in machine vision with multiple applications in pattern recognition [1,3] and medical imaging [6,9,16]. In this paper, we propose a new descriptor which is well suited for grayscale texture classification tasks such as the classification and segmentation B Hind Oulhaj
Information-theoretic wavelet packet subband selection for texture classification
Signal Processing, 2006
Wavelet packet decomposition has been successfully applied to image analysis and classification. The most common approach for wavelet packet-based texture classification is to decompose texture images with wavelet packet transform and to extract energy values for all subbands as features for the subsequent classification. Due to the overcomplete representation provided by the wavelet packet transform, it is suitable to select a set of subbands for sparse representation of the texture for classification. For better classification results, it is desired that the energy features corresponding to the selected subbands are as independent from each other as possible. However, most of the current subband selection methods do not take the dependence between energy values from different subbands into account. In this paper, we investigate the dependence between energy values from different subbands, which may be from the same wavelet basis, or from different wavelet bases. Based on the theoretical analysis and simulation, we propose an information-theoretic measure, mutual information, for selecting subbands for sparse representation of textures for classification. Experimental results show that the proposed method yields a sparse representation of the textures and achieves lower classification error rates than the conventional methods, simultaneously.
Multiscale texture classification using dual-tree complex wavelet transform
Pattern Recognition Letters, 2009
This paper presents a multiscale texture classifier that exploits the Gabor-like properties of the dual-tree complex wavelet transform, shift invariance and 6 directional subbands at each scale, and uses a feature vector comprising of a variance and an entropy at different scales of each of the directional subbands. Experimental results demonstrate its robustness against noise and a higher classification accuracy than a discrete wavelet transform based classifier.
Computers & Electrical Engineering, 2011
This paper proposes a multiscale texture classifier which uses features extracted from both magnitude and phase responses of subbands at different resolutions of the dual-tree complex wavelet transform decomposition of a texture image. The mean and entropy in the transform domain are used to form a feature vector. The proposed method can achieve a high texture classification rate even for small number of samples used in training stage. This makes it suitable for applications where the number of texture samples used in training is very limited. The superior performance and robustness of the proposed classifier is shown for classifying and retrieving texture images from image databases.