Online Image Classifier Learning for Google Image Search Improvement
This paper proposes a content based method to improve image search results from Google search engine.The images returned by Google are used to learn a statistical binary classifier for measuring their relevance to the query.The learning process includes three stages.In the first stage,positive and negative examples are selected from the images by using k-medoids clustering technique.In the second stage,an initial classifier is obtained by performing the Expectation-Maximization (EM)algorithm on positive examples.In the third stage,the Max-Min posterior Pseudo-probabilities (MMP) learning method with dynamic data selection is applied to refine the classifier iteratively.When the classifier learning is completed,all the images are re-ranked in descending order of their posterior pseudo-probabilities.The experimental results show that the proposed approach can bring better image retrieval precisions than original Google results,especially at top ranks.Thus it is helpful to reduce the user labor of browsing the ranking in depth for finding the desired images.
Yuchai Wan Xiabi Liu Jie Bing Yunpeng Chen
School of Computer Science and Technology Beijing Institute of Technology Beijing 100081,China The Middle School Attached to Northern Jiaotong University Beijing 100081,China
国际会议
深圳
英文
103-110
2011-06-06（万方平台首次上网日期，不代表论文的发表时间）