Exploring the Best Classification from Average Feature Combination

Abstract

Feature combination is a powerful approach to improve object classification performance. While various combination algorithms have been proposed, average combination is almost always selected as the baseline algorithm to be compared with. In previous work we have found that it is better to use only a sample of the most powerful features in average combination than using all. In this paper, we continue this work and further show that the behaviors of features in average combination can be integrated into thek-Nearest-Neighbor (kNN) framework. Based on the kNN framework, we then propose to use a selection based average combination algorithm to obtain the best classification performance from average combination. Our experiments on four diverse datasets indicate that this selection based average combination performs evidently better than the ordinary average combination, and thus serves as a better baseline. Comparing with this new and better baseline makes the claimed superiority of newly proposed combination algorithms more convincing. Furthermore, the kNN framework is helpful in understanding the underlying mechanism of feature combination and motivating novel feature combination algorithms.

Sampling Based Average Classifier Fusion

“…We use the following features to build the kernels used in SVM classification. These features are popular due to their discriminative power in object classification, for example, in [13,17,18]. This makes our conclusions drawn from experiments convincing and meaningful.…”

Research on Vocabulary Sizes and Codebook Universality

“…The advantage of this method is that it is invariant to image rotation and scale. Since its introduction, codebook has become a very popular feature descriptor in image classification [3][4][5][6][7]. However, the classical codebook methods fail to consider the relative position information of these feature points.…”