Leveraging robust signatures for mobile robot semantic localization

摘要

Abstract. This paper describes the participation of the CIII UTN FRC team in the ImageCLEF 2012 Robot Vision Challenge. The challenge was focused on the problem of visual place classification in indoor en-vironments. During the competition, participants were asked to classify images according to the room in which they were acquired, using the information provided by RGB and depth images only. We based our ap-proach on the Fisher Vector representation –a robust signature recently proposed in the literature – and the use of efficient linear classifiers. In order to exploit the information provided by different information chan-nels, we adopted a simple fusion strategy and generated classification scores for each image in the sequence. Two tasks were proposed during the competition: in the first, images had to be classified independently of one another while, in the second, it was possible to exploit the tem-poral continuity of the stream. For the first task, we adopted a simple threshold based classification scheme. For the second, we considered the classification of groups of images instead of single frames. These groups, i.e. temporal segments, were automatically generated based on the visual similarity of the images in the sequence. Our team ranked first on both tasks, showing the effectiveness of the proposed schemes.