Image Scene Classification Based on Fisher Discriminative Analysis and Sparse Coding

Bag of visual word(Bo VW) is widely utilized as an image representation model. However, conventional Bo VW construction methods usually cause large representation errors, lack of spatial information and weak discrimination. In order to overcome these drawbacks, this paper proposes an image scene classification algorithm based on fisher discriminative analysis and sparse coding. Firstly, the non-negative sparse locally linear coding is constructed to encode the local features with their neighbor visual vocabularies, thus to make full use of images' spatial information. Secondly, fisher discriminative analysis is added to construct a non-negative sparse locally linear coding model with fisher discriminative criterion constraint, thus to obtain the discriminative sparse representation of images. The novel model can promote the spatial separability of sparse coefficients and enforce the classification capability of images' sparse representation. Finally, support vector machine(SVM) classifier is combined to perform scene classification. Experimental results show that our algorithm efficiently utilizes spatial information of images and incline to seek images' discrimination representations, thus improves the classification performance and is more suitable for image classification.