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MyPlaces: detecting important settings in a visual diary

Blighe, Michael and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2008) MyPlaces: detecting important settings in a visual diary. In: CIVR 2008 - ACM International Conference on Image and Video Retrieval, 07-09 July 2008, Niagara Falls, Canada. ISBN 978-1-60558-070-8

Abstract
We describe a novel approach to identifying specific settings in large collections of passively captured images corresponding to a visual diary. An algorithm developed for setting detection should be capable of detecting images captured at the same real world locations (e.g. in the dining room at home, in front of the computer in the office, in the park, etc.). This requires the selection and implementation of suitable methods to identify visually similar backgrounds in images using their visual features. We use a Bag of Keypoints approach. This method is based on the sampling and subsequent vector quantization of multiple image patches. The image patches are sampled and described using Scale Invariant Feature Transform (SIFT) features. We compare two different classifiers, K Nearest Neighbour and Multiclass Linear Perceptron, and present results for classifying ten different settings across one week’s worth of images. Our results demonstrate that the method produces good classification accuracy even without exploiting geometric or context based information. We also describe an early prototype of a visual diary browser that integrates the classification results.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Engineering > Imaging systems
Computer Science > Image processing
Computer Science > Information retrieval
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Publisher:Association for Computing Machinery
Official URL:http://doi.acm.org/10.1145/1386352.1386382
Copyright Information:© ACM, 2008. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 2008 international conference on Content-based image and video retrieval, {Pages 195-204, (2008)} http://doi.acm.org/10.1145/1386352.1386382
Funders:Science Foundation Ireland
ID Code:641
Deposited On:08 Oct 2008 16:15 by Noel Edward O'connor . Last Modified 08 Nov 2018 16:06
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