Analyzing the Effects of Lowlevel Features for Visual Attribute Recognition

Abstract

In recent years, visual attributes became a popular topic of computer vision research. Visual attributes are being used on various tasks including object recognition, people search, scene recognition, and so on. In order to encode the visual attributes, a common applied procedure for supervised learning of attributes is to extract low-level visual features from the images first. Then, an attribute learning algorithm is applied and visual attribute models are formed. In this thesis, we explore the effects of using different low-level features on learning visual attributes. For this purpose, we use various low-level features, which aim to capture different visual characteristics, such as shape, color and texture. In addition, we also evaluate the effect of the recently evolving deep features on the attribute learning problem. Experiments have been carried out on four different datasets, which were collected for different visual recognition tasks and extensive evaluations have been reported.