Abstract

Content-based retrieval systems leverage low level features such as color, texture or local information of images to find similar images to a respective query image. In recent years the Bag of Visual Words (BoVW) approach, which relies on quantized visual information around local image patches, has gained importance in image retrieval. In this paper we focus on fuzzy algorithms, in order to improve the descriptiveness of image descriptors. We extend the BoVW approach by applying fuzzy clustering and fuzzy assignment to take a step towards more effective visual descriptors, which are matched against each other in content-based similarity searches.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.