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Integrating visual words as bunch of n-grams for effective biomedical image classification.
The Bag-of-Visual-Words (BoVW) has been frequently used in the classification of image data. However, this modeling approach does not take into consideration the spatial relationships of these words, which is important for similarity measurement between images. We have developed a novel technique to incorporate spatial information of visual words based on the n-grams representation. The method encodes regional layout with a 2-gram representation in the local keypoint neighborhood. The region is divided in two zones to capture the relative orientations of pair-wise visual words. In turn, each image is described by an accumulated vector of 2-grams. Then, we compute the Shannon entropy over a random “bunch” of 2-grams to reduce the dimensionality of the feature vector. We discovered that this reduction technique creates a more discriminative feature vector as well as presents a considerable dimensionality reduction of up to 99%. The final representation is a compact and efficient local image descriptor that encodes frequency and arrangement of visual words. The proposed approach was tested by classifying a standard biomedical image dataset into categories defined by image modality and body part. The experimental results demonstrate the importance of contextual relations of visual words. Our proposed approach improved the classification accuracy compared to the traditional BoVW by 6.03%.