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摘要
This paper proposes discriminant kernel assignment (DKA) in the bag-of-features framework for image representation. DKA slightly modifies existing kernel assignment to learn width-variant Gaussian kernel functions to perform discriminant local feature assignment. When directly applying gradient-descent method to solve DKA, the optimization may contain multiple time-consuming reassignment implementations in iterations. Accordingly, we introduce a more practical way to locally linearize the DKA objective and the difficult task is cast as a sequence of easier ones. Since DKA only focuses on the feature assignment part, it seamlessly collaborates with other discriminative learning approaches, e.g., discriminant dictionary learning or multiple kernel learning, for even better performances. Experimental evaluations on multiple benchmark datasets verify that DKA outperforms other image assignment approaches and exhibits significant efficiency in feature coding.
