## 7 Conclusions  
We implemented a novel scene graph generation technique that exploits modern object detection and image captioning frameworks to systematically generate region-specific captions and merge them to create a rich concept graph. This method does not require any graph-based ground truth for training. The proposed approach uses the YOLO-v3-based region-of-interest extraction combined with Show, Attend, and Tell-based image caption generation to generate region-specific captions. We proposed a novel visual-information-specific graph generation using dependency parse trees generated from sentences. As it is a rule-based approach, it adheres to the principles of explainable AI. An efficient graph merging technique using the approximate maximal common subgraph was used to combine the region graphs and compute the holistic scene graph. The major drawback of the proposed approach is the bottleneck of the third-party detection and caption generation framework. However, due to the plug-and-play nature of the approach, these frameworks may be swapped out with more robust counterparts. It was also not possible to quantify the correctness of the graph generation as the process is not dependent on any graph-based supervision. The process of converting region captions to region graphs is deterministic and is solely dependent on the success of the caption generator. It provides a very different take  
Figure 19: Holistic scene graph showing caption generator making wrong assumptions about objects beyond the bounding box.  
Figure 20: Holistic graph generation showing misclassification of partially occluded objects.  
compared to the existing literature, which is either guided by graph-based supervision or by corpus-based knowledge. The novelty of the work is stabilizing the workflow consisting of region-of-interest detection, caption generation, dependency parsing, subgraph generation, and graph combination. The result is a rich graph consisting of nodes corresponding to objects, actions, and their attributes, while the edges provide insights about various types of spatial- or action-based relations among the nodes. The proposed holistic scene graph can be used for various tasks such as content-based image retrieval, visual question answering, image clustering, data warehousing, search engine optimization, and so on. Future works can involve the refinement of the holistic scene graphs by mapping nodes and edges to visual features learned from the image.  
Conceptualization, S.G.; methodology, S.G.; validation, S.G. and T.G.; formal analysis, S.G.; investigation, S.G.; writing--original draft, S.G.; writing--review and editing, T.G. and N.D.; visualization, S.G.; supervision, T.G. and N.D.; project administration, N.D.; funding acquisition, N.D. All authors have read and agreed to the published version of the manuscript. This work funded by the Science and Engineering Research Board (DST), Govt. of India (Ref. EEQ/2018/000963). The authors are thankful to CMATER Laboratory, Dept. of CSE, Jadavpur University, for providing infrastructural support for this work. The authors declare no conflict of interest. The authors declare no conflict of interest.