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| 基于内容图像检索的特征子空间抽取 |
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苏中1,2, 马少平1,2, 张宏江3
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1.清华大学,计算机科学与技术系,北京,100084;2.清华大学,智能技术与系统国家重点实验室,北京,100084;3.微软亚洲研究院,北京,100080
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| 摘要: |
| 作为一种有效的解决手段,相关反馈(relevance feedback)技术在基于内容图像检索(content based image retrieval)的研究中得到了深入的发展.尽管有效,已有的反馈算法却始终没有解决特征空间的有指导降维和特征中的噪声去除这两个问题.提出了一种新的方法,通过对用户在检索过程中提供的正反馈样本在各特征空间中的分布特性,利用主成分分析(principal component analysis)来消除特征中的噪声,实现了对特征空间进行有效的降维.试验结果显示,该方法在不牺牲检索精度的前提下提高了检索速度,降低了存储复杂度. |
| 关键词: 基于内容的图像检索 主成分分析 相关反馈 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.69823001 (国家自然科学基金); the National Grand Fundamental Research 973 Program of China under Grant No.G1998030509 (国家重点基础研究发展规划(973)) |
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| Feature Subspaces Extraction for Content-Based Image Retrieval |
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LUAN Jun-Feng,ZHU Da-Ming,MA Shao-Han
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| Abstract: |
| Relevance feedback (RF) is used as an effective solution for content-based image retrieval (CBIR). Although it is effective, the RF-CBIR framework does not address the issue of feature extraction for dimension reduction and noise reduction. In this paper, a novel method is proposed for extracting features for the class of images represented by the positive images provided by subjective RF. Principal component analysis (PCA) is used to reduce both noise contained in the original image features and dimensionality of feature spaces. The method increases the retrieval speed and reduces the memory significantly without sacrificing the retrieval accuracy. |
| Key words: content-based image retrieval (CBIR) principal component analysis (PCA) relevance feedback |
