Aplication and investigation of unsupervised learning methods in retrieval and classification tasks

Abstract

Unsupervised Learning methods have been established as a solution to increase the effectiveness of content-based searches without requiring user intervention. These methods exploit contextual relationship among images, usually encoded in the distance/similarity information of the collections.This research project intends to investigate the application of such methods in new and diversified domains. Unsupervised learning methods reevaluate the similarity between the elements of the collection and can be used as a pre-processing step in classification tasks. In addition, initial results indicate that the methods can be applied in general multimedia and multimodal retrieval scenarios, considering audio and video.Therefore, the central objective of the proposed project is to deepen such investigation, expanding the domains of application of unsupervised learning. methods. (AU)