计算机工程与应用2011，Vol.47Issue(2)：1-4,18,5.DOI:10.3778/j.issn.1002-8331.2011.02.001
多层次SIFT特征在语义概念检测中的应用
Application of multiple-layer SIFT to semantic concept detection.
摘要
Abstract
The Scale Invariant Feature Transform(SIFT) has been widely used in video concept detection. A lot of researches about SIFT have been done,such as PCA-SIFT, SURF and MESR.But there are few attentions about the influence of different down-sampling ratios to SIFT extraction. Based on the analysis of the influence of different down-sampling ratios to SIFT extraction, and a Multiple-Level SIFT(ML-SIFT) method for senmanfic concept detection is proposed. Experiments on Caltech256 and SceneC1ass13 show that MAPs of ML-SIFT outperform MAPs of SIFT on Caltech256 and SceneC1ass13 by 15.7％ and 5.1％ respectively. In addition,when training the modols using different ratios of positive and negative samples,the performances of ML-SIFT are stable. At the same time, the comparison of SIFT, SURF and ML-SIFT is given in the paper.From the experiments,the performances of SIFT and SURF are similar,but when comparing to ML-SIFT,their performances are worse than ML-SIFT. From above analysis,the ML-SIFT algorithm is effective.关键词
尺度不变特征转换/支持向量机/多层次的尺度不变特征转换/概念检测/特征融合Key words
Scale Invariant Feature Transform (SIFT)/ Support Vector Machine (SVM)/ Multiple-Level SIFT (ML-SIFT)/ semantic detection/feature fusion分类
信息技术与安全科学引用本文复制引用
高赞,赵志诚,蔡安妮,谢晓辉..多层次SIFT特征在语义概念检测中的应用[J].计算机工程与应用,2011,47(2):1-4,18,5.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60772114,No.90920001). （the National Natural Science Foundation of China under Grant No.60772114,No.90920001）
