The bag-of-features (BoF) model for image classification has been thoroughly studied over the last decade. Different from the widely used BoF methods which model images with a pre-trained codebook, the alternative codebook-free image modeling method, which we call codebookless model (CLM), attracts little attention. In this paper, we present an effective CLM that represents an image with a single Gaussian for classification. By embedding Gaussian manifold into a vector space, we show that the simple incorporation of our CLM into a linear classifier achieves very competitive accuracy compared with state-of-the-art BoF methods (e.g., Fisher Vector). Since our CLM lies in a high-dimensional Riemannian manifold, we further propose a joint learning method of low-rank transformation with support vector machine (SVM) classifier on the Gaussian manifold, in order to reduce computational and storage cost. To study and alleviate the side effect of background clutter on our CLM, we also present a simple yet effective partial background removal method based on saliency detection. Experiments are extensively conducted on eight widely used databases to demonstrate the effectiveness and efficiency of our CLM method. HighlightsWe show that codebookless model (CLM) is a very competitive alternative to the mainstream BoF model.Two well-motivated parameters are introduced to further improve our CLM.A joint low-rank learning and SVM is proposed on Gaussian manifold.The comprehensive experiments demonstrated the promising performance of our CLM.
Nakayama al @cite_0 also represented an image with a global Gaussian for scene categorization. However, they matched two Gaussian models by using the Kullback-Leibler (KL) divergence, and hence kernel-based classifiers have to be used. This method is not scalable and has high computational cost. In contrast to @cite_0 , our metric is decoupled which allows a linear classifier to be combined, which makes our method more efficient and scalable than the KL kernel based one in @cite_0 . Moreover, compared with the ad-hoc linear kernel (Euclidean baseline) in @cite_0 , our method takes advantage of the geometry structure of Gaussian models and brings large performance improvement.
@cite_0
2142172505
We present a framework for the classification of visual processes that are best modeled with spatio-temporal autoregressive models. The new framework combines the modeling power of a family of models known as dynamic textures and the generalization guarantees, for classification, of the support vector machine classifier. This combination is achieved by the derivation of a new probabilistic kernel based on the Kullback-Leibier divergence (KL) between Gauss-Markov processes. In particular, we derive the KL-kernel for dynamic textures in both 1) the image space, which describes both the motion and appearance components of the spatio-temporal process, and 2) the hidden state space, which describes the temporal component alone. Together, the two kernels cover a large variety of video classification problems, including the cases where classes can differ in both appearance and motion and the cases where appearance is similar for all classes and only motion is discriminant. Experimental evaluation on two databases shows that the new classifier achieves superior performance over existing solutions.