Paper Info
Title
Robust Keypoint Detection Using Higher-Order Scale Space Derivatives: Application to Image Retrieval
Abstract
Image retrieval has been extensively studied over the last two decades due to the increasing demands for the effective use of multimedia data. Among various approaches to image retrieval, scale space representation and local keypoint descriptors have been shown to be a promising approach. Even though the concept of scale space representation has been known for a long time, it has now gained prominence as a powerful method for image retrieval mostly due to the invention of the Scale Invariant Feature Transform (SIFT). We will review the characteristics of the scale space operation and provide an extended method of scale space operation that significantly improves the image matching accuracy in the context of image retrieval. We use an operational tattoo image database containing 1,000 near duplicate images to show the superior retrieval performance of the proposed method compared to SIFT keypoints.
Year
DOI
Venue
2014
10.1109/LSP.2014.2321755
IEEE Signal Process. Lett.
Keywords
Field
DocType
scale space,image representation,scale invariant feature transform,image matching,multimedia databases,visual databases,higher-order scale space derivatives,scale space representation,local keypoint descriptors,image retrieval,robust keypoint detection,keypoint,transforms,sift,multimedia data,operational tattoo image database,computer vision,accuracy
Computer vision,Scale-invariant feature transform,Automatic image annotation,Feature detection (computer vision),Pattern recognition,Image matching,Image texture,Image retrieval,Scale space,Artificial intelligence,Mathematics,Visual Word
Journal
Volume
Issue
ISSN
21
8
1070-9908
Citations 
PageRank 
References 
4
0.40
10
Authors
3
Name
Order
Citations
PageRank
Unsang Park181536.32
Jong-Seung Park28114.78
Anil Jain3335073334.84