A novel feature selection based semi-supervised method for image classification

Automated surface inspection of products as part of a manufacturing quality control process involves the applications of image processing routines to segment regions of interest (ROI) or objects which correspond to potential defects on the product or part. In these type of applications, it is not known in advance how many ROIs may be segmented from images, and so classification algorithms mainly make use of only image-level features, ignoring important object-level information. In this paper, we will investigate how to preprocess high-dimensional object-level features through a unsupervised learning system and present the outputs of that system as additional image-level features to the supervised learning system. Novel semi-supervised approaches based on K-Means/Tabu Search(TS) and SOM/Genetic Algorithm (GA) with C4.5 as supervised classifier have been proposed in this paper. The proposed algorithms are then applied on real-world CD/DVD inspection system. Results have indicated an increase in the performance in terms of classification accuracy when compared with various existing approaches.