An Integrated Framework for Information Identification With Image Data Using Multi-Technique Feature Extraction

Abstract

Image data has portrayed immense potential as a resourceful foundation of information in current context for numerous applications including biomedicine, military, commerce, education, and web image classification and searching. The scenario has kindled the requirement for efficient content-based image identification from the archived image databases in varied industrial and educational sectors. Feature extraction has acted as the backbone to govern the success rate of content-based information identification with image data. The chapter has presented two different techniques of feature extraction from images based on image binarization and morphological operators. The multi-technique extraction with radically reduced feature size was imperative to explore the rich set of feature content in an image. The objective of this work has been to create a fusion framework for image recognition by means of late fusion with data standardization. The work has implemented a hybrid framework for query classification as a precursor for image retrieval which has been so far the first of its kind.