Accession Number:

AD1039801

Title:

Predicting Good Features for Image Geo-Localization Using Per-Bundle VLAD (Open Access)

Descriptive Note:

Journal Article

Corporate Author:

University of North Carolina at Chapel Hill Chapel Hill United States

Personal Author(s):

• Kim, Hyo Jin
• Dunn,Enrique
• Frahm,Jan-Michael

Report Date:

2016-02-18

Pagination or Media Count:

9.0

Abstract:

We address the problem of recognizing a place depicted in a query image by using a large database of geo-tagged images at a city-scale. In particular, we discover features that are useful for recognizing a place in a data-driven manner, and use this knowledge to predict useful features in a query image prior to the geo-localization process. This allows us to achieve better performance while reducing the number of features. Also, for both learning to predict features and retrieving geo-tagged images from the database, we propose per-bundle vector of locally aggregated descriptors PBVLAD, where each maximally stable region is described by a vector of locally aggregated descriptors VLAD on multiple scale-invariant features detected within the region. Experimental results show the proposed approach achieves a significant improvement over other baseline methods.

Descriptors:

• supervised machine learning
• dimensionality reduction
• geographic regions
• photo sharing websites
• feature extraction
• classification
• clustering
• algorithms
• internet
• databases
• training
• image processing
• information retrieval
• DATA VISUALIZATION

Subject Categories:

• Information Science

Distribution Statement:

APPROVED FOR PUBLIC RELEASE