Event recognition in photo albums using probabilistic graphical models and feature relevance |
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Institution: | 2. College of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu, China 211167;3. Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China 210029 |
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Abstract: | This paper proposes a method for event recognition in photo albums which aims at predicting the event categories of groups of photos. We propose a probabilistic graphical model (PGM) for event prediction based on high-level visual features consisting of objects and scenes, which are extracted directly from images. For better discrimination between different event categories, we develop a scheme to integrate feature relevance in our model which yields a more powerful inference when album images exhibit a large number of objects and scenes. It allows also to mitigate the influence of non-informative images usually contained in the albums. The performance of the proposed method is validated using extensive experiments on the recently-proposed PEC dataset containing over 61 000 images. Our method obtained the highest accuracy which outperforms previous work. |
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Keywords: | Photo albums Event recognition Object/scene relevance Probabilistic graphical models (PGM) |
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