Abstract
Analyzing personal photo albums for understanding the related events is an emerging trend. A reliable event recognition tool could suggest appropriate annotation of pictures, provide the context for single image classification and tagging, achieve automatic selection and summarization, ease organization and sharing of media among users. In this paper, a novel method for fast and reliable event-type classification of personal photo albums is presented. Differently from previous approaches, the proposed method does not process photos individually but as a whole, exploiting three main features, namely Saliency, Gist, and Time, to extract an event signature, which is characteristic for a specific event type. A highly challenging database containing more than 40.000 photos belonging to 19 diverse event-types was crawled from photo-sharing websites for the purpose of modeling and performance evaluation. Experimental results showed that the proposed approach meets superior classification accuracy with limited computational complexity.
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