基于内容图像检索的特征子空间抽取

Feature Subspaces Extraction for Content-Based Image Retrieval

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.

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.