Zengchang Qin

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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)
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 mid-level features to build an effective image retrieval system based on the bag-offeatures (BoF) model. Specifically, we study two(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)
We establish a new set of features for differentiating benign from malignant breast lesions using ultrasound (US) images. Two types of features (sonographic and textural features) are considered. Among them, three sonographic features are novel. Sonograms of 321 pathologically proven breast cases are analyzed and classified into benign and malignant(More)
Tree induction is one of the most effective and widely used models in classification. Unfortunately, decision trees such as C4.5 have been found to provide poor probability estimates. By the empirical studies, Provost and Domingos found that probability estimation trees (PETs) give a fairly good probability estimation. However, different from normal(More)
I Declaration This dissertation is submitted to the University of Bristol in accordance with the requirements of the degree of Master of Science in the Faculty of Engineering. It has not been submitted for any other degree or diploma of any examining body. Except where specifically acknowledged, it is all work of the Author. Labs-Bristol). Some work had(More)
In this paper, we propose a novel approach for crossmodal multimedia retrieval by jointly modeling the text and image components of multimedia documents. In this model, the image component is represented by local SIFT descriptors based on the bag-of-feature model. The text component is represented by a topic distribution learned from latent topic models(More)
To diagnose breast cancer (BCa), the number of mitotic cells present in tissue sections is an important parameter to examine and grade breast biopsy specimen. The differentiation of mitotic from non-mitotic cells in breast histopathological images is a crucial step for automatical mitosis detection. This work aims at improving the accuracy of mitosis(More)