ALADDIN: A locality aligned deep model for instance search

Abstract

Most instance search systems are based on modeling local features. It remains a challenge to apply deep learning techniques into this task because of the asymmetrical similarity between the query region and dataset images. In this paper, we propose ALADDIN, A Locality Aligned Deep moDel for INstance search. This model deals with the asymmetrical similarity by searching query instances at the scale of aligned target regions instead of the whole image. Towards discriminative region representations, we utilize a deep convolutional network which captures both intra-class and inter-class distinctions of the regions. In addition, we propose a semi-supervised method to collect appropriate data to train the network. Extensive experiments confirm that our method is more suitable for generic instance search than most conventional methods, and outperforms the best CNNs-based method in both accuracy and efficiency.