Sparse Representations and Distance Learning for Attribute Based Category Recognition

While traditional approaches in object recognition require the specification of training examples from each class and the application of class specific classifiers, in real world situations, the immensity of the number of image classes makes this task daunting. A novel approach in object recognition is attribute based classification, where instead of training classifiers for the recognition of specific object class instances, classifiers are trained on attributes of the object images and these attributes are subsequently used for the object recognition. The attributes based paradigm offers significant advantages including the ability to train classifiers without any visual examples. We begin by discussing a scenario for object recognition on mobile devices where the attribute prediction and the attribute-to-class mapping are decoupled in order to meet the specific resource constraints of mobile systems. We next present two extensions on the attribute based classification paradigm by introducing alternative approaches in attribute prediction and attribute-to-class mapping. For the attribute prediction, we employ the recently proposed Sparse Representations Classification scheme that offers significant benefits compared to the previous SVM based approaches, such as increased accuracy and elimination of the training stage. For the attribute-to-class mapping, we employ a Distance Metric Learning algorithm that automatically infers the significance of each attribute instead of assuming uniform attribute importance. The benefits of the proposed extensions are validated through experimental results.