In our proposed research work, the application of live-wire algorithm has been proposed to segment complex Aerial insulator images along with RP algorithm to extract the features of images. Firstly, Gray Level Co-occurrence Matrix (GLCM) is employed to extract the texture features of image by the rapid Gray Level Co-occurrence Integrated Algorithm (GLCIA). We have categorised extracted texture feature into two: one with the stronger discriminative ability and other with weaker ability. The weaker discriminative ability has optimized by using PCA & RP. Successfully segmenting the complex low contrast aerial images is one of the main focus of this paper.After optimization by PCA, segmentation is done by Global Minimization with Texture Descriptor (GMTD).After optimization by RP, segmentation is done by Live-wire. To analyze the comparative effect of algorithms on optimization & segmentation, we have used Brodatz dataset. We have observed that Random projection is computationally faster than PCA due to random selection of ‘best’ basis vector which improves the computational speed of RP. Live wire is use to fast segmentation as well as improves weight parameter. Our results demonstrate a 20% improvement in overall system speed and 10% improvement in segmentation accuracy when compared with traditional algorithms .Another advantage of using this technique is that the process is fully automatic thus can be used for training of machine learning and AI based algorithms.

GLCM,GLCIA,Segmentation,GMTD

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