Senmiao Yuan

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In this paper a new method of classification of image in medical domain was introduced. Since the traditional way of diagnosis is slow, time-consuming and heavy-workload, the result is largely influenced by the experience of doctor. The new way of classification can largely improve the efficiency and accuracy in diagnosis. The method is based on Naive(More)
A learning system is one of the future directions of the evolution of content-based image retrieval (CBIR) system. Relevance feedback (RF) is a technique that enables systems to learn from users. In the past few years, this technique has been used as an effective solution for content-based image retrieval. Based on information theory, this paper proposes an(More)
This paper proposes a neural network ensemble model for classification of incomplete data. In the method, the incomplete dataset is divided into a group of complete sub datasets, which is then used as the training sets for the neural networks. Compared with other methods dealing with missing data in classification, the proposed method can utilize all the(More)
Relevance feedback (RF) is a powerful technique in content-based image retrieval (CBIR) system and has become a very active research topic in the past few years. At the early stage of CBIR, research primarily focused on exploring various feature representation and ignored the subjectivity of human perception. There exists a gap between high-level concepts(More)