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An image comprises information, such as color, texture, shape, and intensity, which humans use in parallel for perception. Based on this knowledge, three methods of constructing visual codebook ensembles are proposed in this paper. The first technique introduced diverse individual visual codebooks by randomly choosing interesting points. The second technique was based on a random subtraining image...
In this paper, the advantages of ensemble methods are applied to image categorization. A novel method is introduced for image categorization by combining various visual vocabularies with different sizes in the popular vocabulary approach. The vocabulary approach describes an image as a bag of discrete visual codewords, where the frequency distributions of these words are used for image categorization...
In this paper, the advantages of ensemble methods are adapted to image categorization. A novel method is introduced for image categorization by constructing vocabulary ensembles using different clustering algorithms in the popular vocabulary approach. The vocabulary approach describes an image as a bag of discrete visual words, where the frequency distributions of these words are used for image categorization...
Many interesting point detectors have been proposed in the literature. It is unclear which detectors are more appropriate and how their performance depends on the task. We propose to use different detectors to gain different cues of images. Then an ensemble of classifications can be obtained, each based on one cue. The use of classification ensemble to categorize new images can lead to improved performance...
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