  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.
