A fusing algorithm of Bag-Of-Features model and Fisher linear discriminative analysis in image classification

A fusing image classification algorithm is presented, which uses Bag-Of-Features model (BOF) as images´ initial semantic features, and subsequently employs Fisher linear discriminative analysis (FLDA) algorithm to get its distribution in a linear optimal subspace as images´ final features. Lastly images are classified by K nearest neighbor algorithm. The experimental results indicate that the image classification algorithm combining BOW and FLDA has more powerful classification performances.