Neighbourhood consistency based deep domain adaption analysis for multi category object detection

Pattern classification in domains that follow dissimilar distribution and where target domain has insufficient labelled samples, requires transfer of knowledge across domains through a process called domain adaption. Deep learning research demonstrates the transferability of deep convolutional features that are activations of intermediate layers of convolutional neural networks for domain adaption. Traditional clustering based domain adaption approaches are practical to handle knowledge transfer scenario. This paper presents a scheme that uses local neighborhoods based consistency analysis of one supervised and another unsupervised model to effectively transfer knowledge using deep features. Contrasting conventional models this approach uses only two models to classify patterns except hard ones. Neighbourhood consistency analysis identifies the hard samples, and is classified using a third model. Experimental analysis has been carried out focusing change on category variation of different samples for train and test cases. The proposed approach yields encouraging experimental result on benchmark domain adaption dataset compared to a deep feature based single support vector machine classifier in terms of state of the art metrics demonstrating effective generalization of source domain information.