Efficient common objects localization based on deep hybrid Siamese network

Abstract

Finding the common objects in a set of images is considered one of the recent challenges in different computer vision tasks. Most of the conventional methods have proposed unsupervised and weakly supervised co-localization methods to find the common objects; however, these methods require producing a huge amount of region proposals. This paper tackles this problem by exploiting supervised learning benefits to localize the common object in a set of unlabeled images containing multiple objects or with no common objects. Two stages are proposed to localize the common objects: the candidate box generation stage and the matching and clustering stage. In the candidate box generation stage, the objects are localized and surrounded by the bounding boxes. The matching and clustering stage is applied on the generated bounding boxes and creates a distance matrix based on a trained Siamese network to reflect the matching percentage. Hierarchical clustering uses the generated distance matrix to find the common objects and create clusters for each one. The proposed method is trained on PASCAL VOC 2007 dataset; on the other hand, it is assessed by applying different experiments on PASCAL VOC 2007 6×2 and Object Discovery datasets, respectively. The results reveal that the proposed method outperforms the conventional methods by 8% to 40% in terms of corloc metric.