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Question classification plays an important role in question answering. Features are the key to obtain an accurate question classifier. In contrast to Li and Roth (2002)'s approach which makes use of very rich feature space, we propose a compact yet effective feature set. In particular, we propose head word feature and present two approaches to augment(More)
Despite progress in image retrieval by using low-level features , such as colors, textures and shapes, the performance is still unsatisfied as there are existing gaps between low-level features and high-level semantic concepts (semantic gaps). In this research, we propose a novel image retrieval system based on bag-of-features (BoF) model by integrating(More)
Label semantics is a random set framework for modelling with words. In previous work, several machine learning algorithms based on this semantics have been proposed and studied. In this paper, we introduce a new linguistic rule induction algorithm based on Quinlan's FOIL. According to this algorithm, a set of linguistic rules are generated for(More)
Keywords: Bag-of-features (BoF) Image retrieval Weighted K-means SIFT-LBP HOG-LBP Histogram intersection a b s t r a c t One of the biggest challenges in content based image retrieval is to solve the problem of " semantic gaps " between low-level features and high-level semantic concepts. In this paper, we aim to investigate various combinations of(More)