Image retrieval using dictionary similarity measure View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03

AUTHORS

Raju Ranjan, Sumana Gupta, K. S. Venkatesh

ABSTRACT

Measuring similarity between images is required in many multimedia applications such as retrieving perceptually similar images. The most widely used metric for measuring the distance between two signal vectors is mean square error. Even though MSE provides mathematical tractability, it is not suitable for detecting perceptual similarity between images. In this paper, we propose a new measure to calculate similarity between images called dictionary similarity measure. It determines the distance between two dictionaries learned with respective image features. Similarity measure using algorithm that we propose in this paper is calculated for various image datasets such as Vision Texture Database—MIT Media Lab, Corel-1000 image dataset, and Standard images (Mandrill, Boat, etc.). Further, we propose algorithms to retrieve perceptually similar images using maximum vote criterion and dictionary similarity measure. Similarity measure that we propose in the paper retrieves perceptually similar images in a computational efficient manner with nearly similar accuracy compared to some of the existing dictionary-based image retrieval techniques. More... »

PAGES

313-320

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11760-018-1359-9

DOI

http://dx.doi.org/10.1007/s11760-018-1359-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106705947


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