Translator Disclaimer
3 March 2014 A comparison of histogram distance metrics for content-based image retrieval
Author Affiliations +
Proceedings Volume 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014; 90270O (2014) https://doi.org/10.1117/12.2042359
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States
Abstract
The type of histogram distance metric selected for a CBIR query varies greatly and will affect the accuracy of the retrieval results. This paper compares the retrieval results of a variety of commonly used CBIR distance metrics: the Euclidean distance, the Manhattan distance, the vector cosine angle distance, histogram intersection distance, χ2 distance, Jensen-Shannon divergence, and the Earth Mover’s distance. A training set of ground-truth labeled images is used to build a classifier for the CBIR system, where the images were obtained from three commonly used benchmarking datasets: the WANG dataset (http://savvash.blogspot.com/2008/12/benchmark-databases-for-cbir.html), the Corel Subset dataset (http://vision.stanford.edu/resources_links.html), and the CalTech dataset (http://www.vision.caltech.edu/htmlfiles/). To implement the CBIR system, we use the Tamura texture features of coarseness, contrast, and directionality. We create texture histograms of the training set and the query images, and then measure the difference between a randomly selected query and the corresponding retrieved image using a k-nearest-neighbors approach. Precision and recall is used to evaluate the retrieval performance of the system, given a particular distance metric. Then, given the same query image, the distance metric is changed and performance of the system is evaluated once again.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qianwen Zhang and Roxanne L. Canosa "A comparison of histogram distance metrics for content-based image retrieval", Proc. SPIE 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014, 90270O (3 March 2014); https://doi.org/10.1117/12.2042359
PROCEEDINGS
9 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

Similarity-based retrieval of images using color histograms
Proceedings of SPIE (December 17 1998)
Using browsing to improve content-based image retrieval
Proceedings of SPIE (October 05 1998)
Color based properties query for CBIR HSV global color...
Proceedings of SPIE (September 30 2011)
Image retrieval using texture features BDIP and BVLC
Proceedings of SPIE (December 19 2001)
New similarity measure for color image indexing
Proceedings of SPIE (January 01 2001)

Back to Top