Comparative study on Content based Image Retrieval based on Gabor Texture Features at Different Scales of Frequency and Orientations

Abstract

Content-Based Image Retrieval (CBIR) systems help users to retrieve relevant images based on their contents such as color and texture. In this paper, a study has been made on the application of Gabor Wavelet Transform for texture classification at different values of the number of scales(S) and the number of orientations (K). Texture features are found by calculating the mean and variation of Gabor filtered image. The image indexing and retrieval are conducted on natural images. Based on experiments, Gabor wavelet at five scales of frequency and four orientations gives better performance than the other commonly used scales and orientations i.e., three scales and four orientations, three scales and six orientations, four scales and five orientations, four scales and six orientations and five scales and six orientations.

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“…Wavelet feature analysis allows for the localization of meaningful signals within an image in time and space and separates these signals from noise 71 . We implement the Gabor wavelet filter, which is a group of wavelets, with each wavelet encompassing energy at specific frequency and orientation 68,72 . Textural and edge features can then be constructed from this data set of energy distributions 68,73 .…”

“…For our feature extraction, we determined wavelet transformation to be ideal. Wavelet transformation is widely used for frequency domain analysis and texture‐based feature analysis of an image 68 . Frequency in an image processing is defined as change and diversity between pixels; for example, contrast between black and white has a high pixel value diversity and thus a high frequency 69,70 .…”

“…68,72 Textural and edge features can then be constructed from this data set of energy distributions. 68,73 The Gabor filter is a Gaussian kernel function governed by a sinusoidal component. 74 The Gabor wavelet transformation formula 73 Gaussian major and minor widths, respectively:…”

“…Wavelet transformation is widely used for frequency domain analysis and texture-based feature analysis of an image. 68 Frequency in an image processing is defined as change and diversity between pixels; for example, contrast between black and white has a high pixel value diversity and thus a high frequency. 69,70 Wavelet feature analysis allows for the localization of meaningful signals within an image in time and space and separates these signals from noise.…”

“…71 We implement the Gabor wavelet filter, which is a group of wavelets, with each wavelet encompassing energy at specific frequency and orientation. 68,72 Textural and edge features can then be constructed from this data set of energy distributions. 68,73 The Gabor filter is a Gaussian kernel function governed by a sinusoidal component.…”