Detecting “In-Play” Photos in Sports News Photo Database

In this paper, we treat with in-play classification of sports news photos as an instance of researches on more sophisticated search methods for large-scale photo news databases. We propose two methods to classify sports news photos into one of the given six sports categories and to discriminate in-play photos from not-in-play ones. One is the two-step method which classifies sports categories first and recognizes in-play conditions next, and the other is the one-step method which classifies them simultaneously. In the proposed methods, we integrate textual features extracted from news articles and image features extracted from photo images by Multiple Kernel Learning (MKL). In the experiment of the two-step method, we obtained 99.33% as the classification rate for the sports category classification which is the first step and 80.75% for the in-play classification which is the second step. On the other hand, in the experiment of the one-step method, we obtained 77.08% which was a little less than the result by the two-step method.