MyPlaces: detecting important settings in a visual diary

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MyPlaces: detecting important settings in a visual diary
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 classif...
Michael Blighe, Noel E. O'Connor
Added 18 Oct 2010
Updated 18 Oct 2010
Type Conference
Year 2008
Where CIVR
Authors Michael Blighe, Noel E. O'Connor
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