Paper Info
Title
High-Speed And Local-Changes Invariant Image Matching
Abstract
In recent years, many variants of key point based image descriptors have been designed for the image matching, and they have achieved remarkable performances. However, to some images, local features appear to be inapplicable. Since theses images usually have many local changes around key points compared with a normal image, we define this special image category as the image with local changes (IL). An IL pair (ILP) refers to an image pair which contains a normal image and its IL. ILP usually loses local visual similarities between two images while still holding global visual similarity. When an IL is given as a query image, the purpose of this work is to match the corresponding ILP in a large scale image set. As a solution, we use a compressed HOG feature descriptor to extract global visual similarity. For the nearest neighbor search problem, we propose random projection indexed KD-tree forests (rKDFs) to match ILP efficiently instead of exhaustive linear search. rKDFs is built with large scale low-dimensional KD-trees. Each KD-tree is built in a random projection indexed subspace and contributes to the final result equally through a voting mechanism. We evaluated our method by a benchmark which contains 35,000 candidate images and 5,000 query images. The results show that our method is efficient for solving local-changes invariant image matching problems.
Year
DOI
Venue
2015
10.1587/transinf.2015EDP7093
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
local-changes invariant, image matching, feature compression, random projection indexed KD-tree forests
Computer vision,Pattern recognition,Computer science,Image matching,Artificial intelligence,Invariant (mathematics)
Journal
Volume
Issue
ISSN
E98D
11
1745-1361
Citations 
PageRank 
References 
2
0.38
17
Authors
2
Name
Order
Citations
PageRank
Chao Zhang1939103.66
Takuya Akashi2209.57