NDLI: Efficacy of Season Prediction for Geo-locations Using Geo-tagged Images

Content Provider	IEEE Xplore Digital Library
Author	Sreenivasamurthy, S. Frank, S.
Copyright Year	2015
Description	Author affiliation: Comput. Sci., Univ. of California, Santa Cruz, Santa Cruz, CA, USA (Sreenivasamurthy, S.; Frank, S.)
Abstract	Tens of thousands of pictures are taken at different locations throughout the year. People often visit places and take pictures to remember their visits. We believe that the seasonal travel patterns of people to specific locations will create a correlation between a location and the season of the images taken in that location. For example, fewer people visit Bear Valley, California during the summer than during the winter as it is a popular Ski Resort. Therefore, we believe we will find more pictures of Bear Valley taken during winter when compared to any other seasons. Today, most of the photographs have geo-location (Latitude and Longitude) and time when the photograph was taken included in their metadata. Given the distribution of photographs, correlations between locations and seasons found using this metadata could potentially be used to develop a system to predict the best time of year to visit particular locations or number of people who may visit a tourist destination in the next season allowing business establishments to prepare appropriately. In this work we evaluate the efficacy of using this metadata to predict the season given the location. In our work, using a dataset comprised of photograph metadata, we focused on approximately 1.1 million photographs taken in California. Using variations of the nearest neighbor algorithm, we were able to predict the season of a photograph with a maximum correctness of 80.9% with a sufficiently large training set. We experimented with both weighted K-Nearest Neighbor (K-NN) and Fixed Radius Nearest Neighbor (FRNN) using no-weight, inverse, logarithmic, and gaussian weight calculators. Using the K-NN model, we found that logarithmic weighted K-NN performed the best at 79.55% correctness. Using the Fixed Radius NN model, we found that the gaussian weighted model using standard deviation of 0.0001 performed the best at 80.9%.
Starting Page	476
Ending Page	484
File Size	1198091
Page Count	9
File Format	PDF
ISBN	9781467366564
DOI	10.1109/IRI.2015.79
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Metadata Prediction algorithms Standards Predictive models Computer science Efficacy or prediction Data-mining Machine-Learning season-prediction geo-tag geo-location KNN FRNN
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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