Clustering of hierarchical image database to reduce inter-and intra-semantic gaps in visual space for finding specific image semantics

• Method to adaptively select branches of clustered hierarchical dataset is proposed.
• A content based retrieval system is developed to give general & specific semantics.
• System reports better precision (78%) on ImageNet compared to Khanna et al. (≈69%).
• On WANG, attained precision is 97.7% compared to 94.2% obtained by Khanna et al.
• The study explores system’s capability in reducing inter- and intra-semantics gap.

Empowering content based systems to assign image semantics is an interesting concept. This work explores semantically categorized image database and forms a hierarchical visual search space. Overlapping of visual features of images from different categories and subcategories are possible reasons behind inter-semantic and intra-semantic gaps. Usually each category/node in the image database has a single representation, but variability and broadness of semantic limit the usage of such representation. This work explores the application of agglomerative hierarchical clustering to automatically identify groups within a semantic in the visual space. Visual signatures of dominant clusters corresponding to a node represent its semantic. Adaptive selection of branches on this clustered data facilitates efficient semantic assignment to query image in reduced search cost. Based on the concept, content based semantic retrieval system is developed and tested on hierarchical and non-hierarchical databases. Results showcase capability of the proposed system to reduce inter- and intra-semantic gaps.