Implementation and Evaluation of Image Retrieval Method Utilizing Geographic Location Metadata
Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Multimedia retrieval systems are very important today with millions of content creators all over the world generating huge multimedia archives. Recent developments allows for content based image and video retrieval. These methods are often quite slow, especially if applied on a library of millions of media items.
In this research a novel image retrieval method is proposed, which utilizes spatial metadata on images. By finding clusters of images based on their geographic location, the spatial metadata, and combining this information with existing content- based image retrieval algorithms, the proposed method enables efficient presentation of high quality image retrieval results to system users.
Clustering methods considered include Vector Quantization, Vector Quantization LBG and DBSCAN. Clustering was performed on three different similarity measures; spatial metadata, histogram similarity or texture similarity.
For histogram similarity there are many different distance metrics to use when comparing histograms. Euclidean, Quadratic Form and Earth Mover’s Distance was studied. As well as three different color spaces; RGB, HSV and CIE Lab.
Place, publisher, year, edition, pages
2009. , 99 p.
UPTEC F, ISSN 1401-5757 ; 10069
image retrieval, geographic metadata, histogram, texture, dbscan, vector quantization, color spaces, meda database
IdentifiersURN: urn:nbn:se:uu:diva-171865OAI: oai:DiVA.org:uu-171865DiVA: diva2:512545
Subject / course
Master Programme in Engineering Physics
Nyberg, TomasOrsborn, Kjell