Retina-enhanced SURF descriptors for semantic concept detection in videos

Abstract

This paper proposes to investigate the potential benefit of the use of low-level human vision behaviors in the context of high-level semantic concept detection. A large part of the current approaches relies on the Bag-of-Words (BoW) model, which has proven itself to be a good choice especially for object recognition in images. Its extension from static images to video sequences exhibits some new problems to cope with, mainly the way to use the added temporal dimension for detecting the target concepts (swimming, drinking...). In this study, we propose to apply a human retina model to preprocess video sequences, before constructing a State-Of-The-Art BoW analysis. This preprocessing, designed in a way that enhances the appearance especially of static image elements, increases the performance by introducing robustness to traditional image and video problems, such as luminance variation, shadows, compression artifacts and noise. These approaches are evaluated on the TrecVid 2010 Semantic Indexing task datasets, containing 130 high-level semantic concepts. We consider the well-known SURF descriptor as the entry point of the BoW system, but this work could be extended to any other local gradient based descriptor.