Semantics Preserving Hierarchy based Retrieval of Indian heritage monuments
This work addresses the challenge of accurately categorizing and retrieving images of Indian monuments for cultural heritage preservation, though it is incremental as it builds on existing hierarchical and re-ranking methods.
The paper tackles the problem of classifying and retrieving images of Indian heritage monuments by preserving semantic information through a hierarchical framework, achieving improved performance in both tasks on a newly curated dataset.
Monument classification can be performed on the basis of their appearance and shape from coarse to fine categories. Although there is much semantic information present in the monuments which is reflected in the eras they were built, its type or purpose, the dynasty which established it, etc. Particularly, Indian subcontinent exhibits a huge deal of variation in terms of architectural styles owing to its rich cultural heritage. In this paper, we propose a framework that utilizes hierarchy to preserve semantic information while performing image classification or image retrieval. We encode the learnt deep embeddings to construct a dictionary of images and then utilize a re-ranking framework on the the retrieved results using DeLF features. The semantic information preserved in these embeddings helps to classify unknown monuments at higher level of granularity in hierarchy. We have curated a large, novel Indian heritage monuments dataset comprising of images of historical, cultural and religious importance with subtypes of eras, dynasties and architectural styles. We demonstrate the performance of the proposed framework in image classification and retrieval tasks and compare it with other competing methods on this dataset.