Segmentation and recognition of multi-model photo event

Abstract

Photograph becomes more and more convenient with the rapid growing of smart phones, which makes photo become one of the most widely used social media. In this paper, we have proposed a segmentation and recognition method for multi-model photo event to help users organize and manage their increasing photo collections. Generative model of photo event is built by analyzing time, location, camera parameters and visual content of photos. Expectation-Maximization (EM) learning algorithm is applied to discover the best parameters of the proposed generative model. With the defined model, each photo is categorized into the corresponding event by calculating the maximum posteriori probability. The representativeness of an event is a photo collage constructed by selecting a set of representative photos from the corresponding event. The proposed method is characterized by the following properties: (1) unlike most of the existing photo event segmentation methods, the location of photos is treated as a key feature, (2) the representativeness of an event is a picture collage instead of a single photo, which is not only informative but also very appealing. The experimental results show that the proposed method is effective and efficient on the photo collections from three experienced smart phone users.