Programação genética aplicada à busca de imagens

Abstract

The volume of information encoded in the form of images has increased significantly in the last decades. Contributing to this scenario, the wide-spread use of mobile devices, such as tablets and smartphones, and even notebooks, which not only can take photos, but also easily send them to connected applications, such as web services and social networks. Nowadays, images are used in several applications, such as to record personal moments of people’s life or showing products in e-commerce online stores. As a consequence, not only does the volume of images increase, but also the interest in solutions able to retrieve these images. The main goal of this thesis is to investigate the impact of using genetic programming (GP) as a tool for combining different sources of evidence available when retrieving images. As case studies, we considered the application of GP in two different contexts: image retrieval on the Web using textual information automatically extracted from Web pages, and visual search by expanding the image query using information derived from different types of data, such as text and visual content. We evaluate the proposed expansion strategies in an application of visual search for products focused on e-commerce stores for the fashion domain. Experiments performed in the context of image retrieval on the Web showed that the evolutionary approach outperformed the best baseline with gains of 22.36% in terms of MAP. In the context of visual search for e-commerce applications, experimental results indicated that automatic expansion based on genetic programming is an effective alternative for improving the quality of image search results. When compared to a genetic programming system based only on visual information, the multimodal expansion achieved gains of at least 19% in all scenarios considered. When compared to a similar approach, but completely ad hoc, the GP framework achieved gains of up to 54% in terms of MAP.