CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 11584 > Article
Paper
10 November 2020 Improved KD tree high dimensional index algorithm based on location information
Xiuyang Zhang, Genyuan Zhang
Author Affiliations +
Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 115840S (2020) https://doi.org/10.1117/12.2580714
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China
Abstract
The basic KD tree nearest neighbor search has better retrieval performance for low dimension feature space. However, when there is no time requirement to estimate the parameters from the training data, it is difficult to find the nearest neighbor time in the large training set. The number of observations required in the training data set increases exponentially with the increase of dimension. For the 128 dimensional SIFT feature vector nearest neighbor search in this study, simply using KD tree is not the best choice. So we adopt the method of hierarchical index to solve the problem of large image with global plane constraint information. For large original image, we can assist the next index according to the coarse position information returned by LSH index of upper thumbnail. First, for the lower layer, we need to construct an improved KD tree index structure based on location information. The main idea of KD tree index structure based on position information improvement is to establish a plane coordinate system with the upper left corner of the two-dimensional image plane as the coordinate origin, the horizontal direction as the X axis and the vertical direction as the Y axis. In the process of constructing the fourth resolution of the first five nodes of KD tree, X and Y coordinate information are used alternately to split the nodes. In this way, we can roughly locate the feature matching area to the size of the original image, so as to improve the retrieval accuracy and speed at the same time.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Xiuyang Zhang and Genyuan Zhang "Improved KD tree high dimensional index algorithm based on location information", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 115840S (10 November 2020); https://doi.org/10.1117/12.2580714
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 5 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Feature extraction
Image retrieval
Dimension reduction
Image processing
Binary data
Communication engineering
Content based image retrieval
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top