A spatial class LDA model for classification of sports scene images
Jin Yong Jeon, Munchurl Kim · 2015
Recently, the bag-of-visual words (BoW) models have widely been studied in computer vision area. Owing to the limit of the BoW models that only consider the distributions of visual words in images, the Latent Dirichlet Allocation (LDA) model has drawn an attention to discover the structure of the visual word distributions over latent topics which can represent semantic objects in images. In order to reflect the spatial information of images, the LDA model has been extended to so-called a spatial LDA model for image segmentation, which is not applicable for image classification. Therefore, in this paper, we propose a spatial class LDA (scLDA) model for image classification where the topic distributions over visual words are found per image segments and a class-specific-simplex LDA (cssLDA) model is applied for image classification. From our experimental results, the proposed scLDA model outperforms the previous LDA models in terms of correct classification rates.