GLCM based parallel texture segmentation using a multicore processor

Abstract EN

his paper investigates the using of Gray Level Co-Occurrence Matrix (GLCM) based on supervised texture segmentation.

In most texture segmentation methods, the processing algorithm is applied to a window of the original image rather than to the entire image using sliding scheme.

To attain a good segmentation accuracy especially in the boundaries, optimal size of window is determined, or windows of variant sizes are used.

Both options are very time consuming.

Here, a new technique is proposed to build an efficient GLCM based texture segmentation system.

This scheme uses a fixed window of variant apertures.

This will reduce the computation overhead and recourses that required to compute GLCM, and will improve the segmentation accuracy.

Image's windows are multiplied with a matrix of local operators.

After that, GLCM is computed and features are extracted and classified and the segmented image is produced.

In order to reduce the segmentation time, two similarity metrics are used to classify the texture pixels.

Euclidean metric is used to find the distance between the current and previous GLCM.

If it is above a predefined threshold, then the computation of GLCM descriptors are required.

Gaussian metric is used as a distance measure between two GLCM descriptors.

Furthermore, a median filter is applied to the segmented image.

Finally, the transition and misclassified regions are refined.

The proposed system is parallelized and implemented on a multicore processor.