An Improved Asymmetric Bagging Relevance Feedback Strategy for Medical Image Retrieval

• 作者: Sheng-sheng Wang Yan-ning Shao
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: Social computing CONTENT-BASED image RETRIEVAL Fuzzy support vector machine RELEVANCE feedback IMPROVED ASYMMETRIC BAGGING
• 年，卷（期）: 2016,（1）
• 页码范围: 45-47
• 页数: 3页
• 分类号: C5

摘要

Much attention has been paid to relevant feedback in intelligent computation for social computing, especially in content-based image retrieval which based on WeChat platform for the medical auxiliary. It has a good effect on reducing the semantic gap between high semantics and low semantics of images. There are many kinds of support vector machines (SVM) based relevance feedback methods in image retrieval, but all of them may encounter some problems, such as a small size of sample, an asymmetric positive sample and negative sample as well as a long feedback cycle. To deal with these problems, an improved asymmetric bagging (IAB) relevance feedback algorithm is proposed. Furthermore, we apply a new fuzzy support machine (FSVM) to cooperate with IAB. To solve the over-fitting and real-time problems, we use modified local binary patterns (MLBP) as image features. Finally, experimental results demonstrate that our method performs other methods in terms of improving retrieval precision as well as retrieval efficiency.