Lifelog image clustering is the process of grouping images into events based on image similarities. Until now, groups of images with low variance can be easily clustered, but clustering images with high variance is still a problem. In this paper, we challenge the problem of high variance, and present a methodology to accurately cluster images into their corresponding events. We introduce a new approach based on rank-order distance techniques using a combination of image similarity and an emotional feature measured from a biosensor. We demonstrate that emotional features along with rank-order distance based clustering can be used to cluster groups of images with low, medium, and high variance. Experimental evidence suggests that compared to average clustering precision rate (65.2%) from approaches that only consider image visual features, our technique achieves a higher precision rate (85.5%) when emotional features are integrated.
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