This paper present a texture compression technique for still images based on the wavelet transform and the auto-regressive (AR) texture model in order to increase the compression ratio with a minimal loss of image quality. First the influences of the initial condition and the order of an AR model on the resulting texture model are investigated to serve as a theoretical foundation for the propos...

A novel method for retrieving image based on color and texture extraction is proposed for improving the accuracy. In this research, we develop a novel image retrieval method based on wavelet transformation to extract the local feature of an image, the local feature consist color feature and texture feature. Once an image taking into account, we transform it using wavelet transformation to four ...

In this paper, a database of texture images is analyzed by the Laws’ texture energy measure technique. The Laws’ technique has been used in a number of fields, such as computer vision and pattern recognition. Although most applications use Laws’ convolution filters with sizes of 3× 3 and 5× 5 for extracting image features, our experimental system uses extended resolutions of filters with sizes ...

In this paper, analysis of the feature selection for scale invariance texture image retrieval using fuzzy logic classifier and wavelet and co-occurrence matrix based feature is carried out. Two types of texture features are extracted one using Discrete Wavelet Transform (DWT) and other using Cooccurrence matrix. Energy and Standard Deviation are obtained from each sub-band of DWT coefficients u...

In this paper, we present a novel approach for image retrieval based on extraction of low level features using techniques such as Directional Binary Code (DBC), Haar Wavelet transform and Histogram of Oriented Gradients (HOG). The DBC texture descriptor captures the spatial relationship between any pair of neighbourhood pixels in a local region along a given direction, while Local Binary Patter...

The histogram of image intensities is used extensively for the retrieval of images from visual databases. An obvious way to extend this feature is to compute the histograms of multiple resolutions of an image. Both this extension and the plain histogram are fast to compute, space efficient, invariant to rigid motions, and robust to noise. In addition, the histograms over multiple image resoluti...

Spatial fluctuations in microwave backscatter may be an important piece of information in discriminating tree stands. However, the presence of speckle in synthetic aperture radar (SAR) image data is a barrier to the exploitation of image texture. We explored a new methodology that combines a recent adaptive speckle reduction algorithm by Lopes et al. [12] with a generic texture estimation schem...

Medical disease examination is often based on images. Mining these images in order to obtain the classification knowledge for automatic image classification is a challenging task. This task belongs to the field of image mining. Image mining is usually not only comprised of mining a table of numbers it has also to do with transforming the image in the right image description. Both, the image des...

Photographs and images often have regions which are in some sense flawed. Often, there may be a stain or an undesired feature covering a significant portion of the image, and an algorithm that can “fix” the image is desired. We propose a technique based on Heeger and Bergen’s texture synthesis algorithm [4]. By integrating a composition step into the aforementioned algorithm, one portion of the...

Content based image retrieval is the task of searching images from a database, which are visually similar to a given example image. Since there is no general deenition for visual similarity, there are diierent possible ways to query for visual content. In this work we present methods for content based image retrieval based on texture similarity using interest points and Gabor features. Interest...