Abstract
Although over the past decades there has been remarkable progress in the field of computer vision, scientists are still confronted with the problem of designing techniques and frameworks that can easily scale to many different domains and disciplines. It is true that state of the art approaches cannot produce highly effective models, unless there is dedicated, and thus costly, human supervision in the process of learning. Recently, we have been witnessing the rapid growth of social media (e. g. images, videos, etc. ) that emerged as the result of users’ willingness to communicate, socialize, collaborate and share content. The outcome of this massive activity was the generation of a tremendous volume of user contributed data available on the Web, usually along with an indication of their meaning (i. e. tags). This has motivated researchers to investigate whether the Collective Intelligence that emerges from the users’ contributions inside a Web 2. 0 application, can be used to remove or ease the burden for dedicated human supervision. By doing so, this social content can facilitate scalable but also effective learning. In this thesis we contribute towards this goal by tackling scalability in two ways; first, we opt to gather effortlessly high quality training content in order to facilitate scalable learning to numerous concepts, which will be referred to as system scalability. Towards this goal, we examine the potential of exploiting user tagged images for concept detection under both unsupervised and semi-supervised frameworks. Second, we examine the scalability issue from the perspective of computational complexity, which we will refer to as computational scalability. In this direction, we opt to minimize the computational cost while at the same time minimize the inevitable performance loss by predicting the most prominent concepts to process further.