Visual Keyword-based Image Retrieval using Latent Semantic Indexing, Correlation-enhanced Similarity Matching and Query Expansion in Inverted Index

This paper presents an image retrieval framework with scalable image representation and inverted file-based indexing by incorporating automatically generated visual keywords. A codebook of visual keywords is implemented adopting a self-organizing map (SOM)-based vector quantization on the feature space of segmented image regions. The codebook is utilized to represent images by calculating the keyword statistics in the individual images as well as in the collection as a whole. To reduce the dimensionality of the sparse feature vector, latent semantic indexing technique is applied and a similarity matching function is proposed by exploiting the correlation between visual keywords. A query expansion strategy is also proposed in the inverted index based on the topology preserving structure of the SOM. Experimental results over a collection of 5000 general photographic images demonstrate the efficiency and effectiveness of the proposed approach compared to the low-level histogram-based approaches