Paper Info
Title
Global Warp Metric Distance: Boosting Content-based Image Retrieval through Histograms
Abstract
This work presents a new distance functionthe Global Warp Metric Distance - to compare histograms used as a feature to index image databases in content-based image retrieval environments. The Metric Histogram represents a compact, but efficient alternative to the use of traditional gray-level histograms to represent images. The Global Warp Metric Distance (GWMD) enhances the comparison between histograms, replacing the rigid bin-to-bin evaluation by the Warp method, which allows a local "adjustment" of one histogram to the other during the distance calculation, introducing a global matching of the curves. Besides this, GWMD applies a set of geometric global features of histograms to determine the final distance. Results on similarity retrieval in medical images demonstrate the superiority of the proposed approach in analyzing image sets that present brightness and contrast disparities: it reduces the amount of both false positive and false negative retrievals. Moreover, these results comply with similarity evaluations performed by domain specialists.
Year
DOI
Venue
2005
10.1109/ISM.2005.64
ISM
Keywords
Field
DocType
index image databases,image set,content-based image retrieval environment,global warp metric distance,content-based image retrieval,new distance functionthe,metric histogram,medical image,final distance,distance calculation,warp method,image retrieval,database indexing,false positive,indexation,distance function
Histogram,Computer vision,Automatic image annotation,Pattern recognition,Computer science,Image texture,Metric (mathematics),Image retrieval,Boosting (machine learning),Artificial intelligence,Content-based image retrieval,Visual Word
Conference
ISBN
Citations 
PageRank 
0-7695-2489-3
2
0.43
References 
Authors
7
3
Name
Order
Citations
PageRank
Joaquim Cezar Felipe1547.17
Agma Juci Machado Traina222117.58
Caetano Traina Jr.31052137.26