Co-Occurring of Object Detection and Identification towards unlabeled object discovery

In this paper, we propose a novel deep learning based approach for identifying co-occurring objects in conjunction with base objects in multilabel object categories. Nowadays, with the advancement in computer vision based techniques we need to know about co-occurring objects with respect to base object for various purposes. The pipeline of the proposed work is composed of two stages: in the first stage of the proposed model we detect all the bounding boxes present in the image and their corresponding labels, then in the second stage we perform co-occurrence matrix analysis. In co-occurrence matrix analysis, we set base classes based on the maximum occurrences of the labels and build association rules and generate frequent patterns. These frequent patterns will show base classes and their corresponding co-occurring classes. We performed our experiments on two publicly available datasets: Pascal VOC and MS-COCO. The experimental results on public benchmark dataset is reported in Sec 4. Further we extend this work by considering all frequently objects as unlabeled and what if they are occluded as well.