Design and implementation of a novel visual analysis system for image clasiffication

Altintakan, Ümit Lütfü
Possibilities offered by the technology to create, share and disseminate image and video data have resulted in a rapid increase in the available visual data. However, the data is useless unless it is effectively accessed, which necessitates the semantic analysis of visual data. In this dissertation, we present a novel visual analysis system along with its application to image classification problem. We aim to address the challenges in the area originated from the semantic gap, and to facilitate the research efforts in the extraction of high-level semantic information from images. Our system differs from existing works, and contributes to the area in several aspects: A complete visual analysis system in an integrated architecture, a novel fuzzy learning approach in classifier training, a unique feature weighting scheme, a probabilistic classification method, a new high-level classifier fusion, and a new bag-of words model are some of the key contributions introduced in this dissertation. The experiments conducted on benchmark datasets have shown that our approaches can significantly improve the performance in image classification.