Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6938459 | Journal of Visual Communication and Image Representation | 2016 | 13 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Siham Bacha, Mohand Saïd Allili, Nadjia Benblidia,