Hierarchy of visual features for object recognition

Most approaches for object recognition (OR) use a single feature descriptor to identify the object class from a query image. However, specifically in case of variations in appearance, scale and illumination, the performance of features not only vary depending on the class, but also on the query sample. We propose a biological inspired framework for OR using concepts from feature integration theory (FIT). Our model uses a hierarchy of visual features for OR. The key components in the proposed approach are: (i) SALCUT - unsupervised segmentation for salient object localization; (ii) optimal feature selection - identify appropriate features for each class, at each level of feature hierarchy, for a test instance; (iii) feature combination - which happens at higher levels of feature hierarchy, if features selected at the lower level are unable to classify a test instance. Our method outperforms several state-of-the-art techniques, when validated using two real-world datasets.