RBIR Based on Signature Graph
This work addresses image retrieval efficiency for database applications, but it appears incremental as it builds on existing region-based and signature methods.
The paper tackles image retrieval by developing a region-based method that extracts interest points, builds binary signatures for storage efficiency, and uses a signature graph with Earth Mover's Distance for similarity measurement, achieving results tested on a Corel database of over 10,000 images.
This paper approaches the image retrieval system on the base of visual features local region RBIR (region-based image retrieval). First of all, the paper presents a method for extracting the interest points based on Harris-Laplace to create the feature region of the image. Next, in order to reduce the storage space and speed up query image, the paper builds the binary signature structure to describe the visual content of image. Based on the image's binary signature, the paper builds the SG (signature graph) to classify and store image's binary signatures. Since then, the paper builds the image retrieval algorithm on SG through the similar measure EMD (earth mover's distance) between the image's binary signatures. Last but not least, the paper gives an image retrieval model RBIR, experiments and assesses the image retrieval method on Corel image database over 10,000 images.