Using Neural Network to Combine Measures of Word Semantic Similarity for Image Annotation

摘要：

This paper proposes a Feed-forward Neural Network (FNN)based method to combine word-to-word semantic similarity metrics for improving the accuracy of image annotation.The network fuses various estimates of word similarity to output a hybrid score which is used in the random walker with restarts method of image annotation refinement.A particle swarm optimization algorithm is designed to train the network to achieve the optimal annotation accuracy.Each particle represents a FNN configuration,the fitness value of which is the accuracy evaluation of image annotation based on the corresponding FNN.We conducted the experiments of image annotation on the Corel-5K dataset.The experimental comparisons between single measures and our combined measure show that the proposed method is effective and promising.

作者: Yue Cao Xiabi Liu Jie Bing Li Song

作者单位: Beijing Laboratory of Intelligent Information Technology,School of Computer Science and Technology Beijing Institute of Technology Beijing 100081,China

会议类型: 国际会议

会议名称: 第八届IEEE信息与自动化国际会议(ICIC 2011)

会议地点: 深圳

会议语种:英文

页码: 833-837

在线出版日期: 2011-06-06（万方平台首次上网日期，不代表论文的发表时间）