Latent topic visual language model for object categorization

This paper presents a latent topic visual language model to handle variation problem in object categorization. Variations including different views, styles, poses, etc., have greatly affected the spatial arrangement and distribution of visual features, on which previous categorization models largely depend. Taking the object variations as hidden topics within each category, the proposed model explores the relationship between object variations and visual feature arrangement in the traditional visual language modeling process. With this improvement, the accuracy of object categorization is further boosted. Experiments on Caltech 101 dataset have shown that this model makes sense and is effective.