The Semi-Variogram and Spectral Distortion Measures for Image Texture Retrieval

Abstract

Abstract-Semi-variogram estimators and distortion measures of signal spectra are utilized in this paper for image texture retrieval. On the use of the complete Brodatz database, most high retrieval rates are reportedly based on multiple features, and the combinations of multiple algorithms; while the classification using single features is still a challenge to the retrieval of diverse texture images. The semi-variogram, which is theoretically sound and the cornerstone of spatial statistics, has the characteristics shared between true randomness and complete determinism; and therefore can be used as a useful tool for both structural and statistical analysis of texture images. Meanwhile, spectral distortion measures derived from the theory of linear predictive coding provide a rigorously mathematical model for signal-based similarity matching, and have been proven useful for many practical pattern classification systems. Experimental results obtained from testing the proposed approach using the complete Brodatz database, and the UIUC texture database suggest the effectiveness of the proposed approach as a single-feature-based dissimilarity measure for real-time texture retrieval.

Automated VSS-based Burn Scar Assessment using Combined Texture and Color Features of Digital Images in Error-Correcting Output Coding

“…To increase the sample size for machine learning and validation, each cropped image was divided into 9 sub-images of equal size, giving the total of 24 × 9 = 216 sub-images. This division of images has been a practice adopted for classifying images with limited samples 19 , 20 . Figure 1 shows examples of the cropped images of the burn scars of 8 different VSS total scores.…”

“…In terms of probability, the estimation of the SV does not require the knowledge of the mean of the random function. The SV of an image is defined as 20 where f ( x i ) is the image intensity at x i , h is a distance, and m ( h ) is the total number of pairs of pixels separated by h .…”

Spatial‐spectral analysis method using texture features combined with PCA for information extraction in hyperspectral images

“…Variogram, which is regarded as the cornerstone of spatial statistics, can be used to characterize the spatial continuity as a useful tool for both the structural and statistical analysis of texture images. 16 Variogram is applied to display the variability between data points as a function of distance. Calculation of the semivariogram can be constrained to particular spatial directions.…”

“…Image texture analysis, an important area of research in image processing, aims to provide information about the spatial arrangement of color or intensities in an image. Up to date, the notion of texture is quite subjective because of the lack of formal definition . Conventionally, texture can be categorized into two major classes: regular and irregular .…”

“…Up to date, the notion of texture is quite subjective because of the lack of formal definition. 16 Conventionally, texture can be categorized into two major classes: regular and irregular. 17 Many techniques have been proposed for the retrieval of image texture.…”