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

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.