Enhancing Computer Vision Using the Collective Intelligence of Social Media
Teaching the machine has been a great challenge for computer vision scientists since the very first steps of artificial intelligence. Throughout the decades there have been remarkable achievements that drastically enhanced the capabilities of the machines both from the perspective of infrastructure (i.e., computer networks, processing power, storage capabilities), as well as from the perspective of processing and understanding of the data. Nevertheless, computer vision scientists are still confronted with the problem of designing techniques and frameworks that will be able to facilitate effortless learning and allow analysis methods to easily scale in 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 that dictates the relation between the content and its meaning (i.e., annotation). Recently, we have been witnessing the rapid growth of Social Media 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 that have been made available on the Web, usually along with an indication of their meaning (i.e., tags). This has motivated the research objective of investigating whether the Collective Intelligence that emerges from the users’ contributions inside a Web 2.0 application, can be used to remove the need for dedicated human supervision during the process of learning. In this chapter we deal with a very demanding learning problem in computer vision that consists of detecting and localizing an object within the image content. We present a method that exploits the Collective Intelligence that is fostered inside an image Social Tagging System in order to facilitate the automatic generation of training data and therefore object detection models. The experimental results shows that although there are still many issues to be addressed, computer vision technology can definitely benefit from Social Media.