关键词: 主成分分析, 余弦度量, 欧氏距离, 局部信息, 基于内容的图像检索

Abstract: Focusing on the issues that the Principal Component Analysis (PCA) related dimensionality reduction methods are limited to deal with nonlinear distributed datasets and have poor robustness, a new dimensionality reduction method named Robust Cosine-Euclidean Metric (RCEM) was proposed. Considering that Cosine Metric (CM) can handle the outliers efficiently and Euclidean distance can well maintain variance information of samples, the CM was used to describe the geometric characteristics of neighborhood and the Euclidean distance was used to depict the global distribution of dataset. This new proposal method retained local information of dataset while achieving the unification of local and global structure, thus it increased the robustness of local dimensionality reduction algorithm and helped avoiding the problem of small sample size cases. The experimental results on Corel-1000 dataset showed that the retrieval average precision of RCEM was 5.61% higher than that of Angle Optimization Global Embedding (AOGE), and the retrieval time of RCEM was decreased by 42% compared with dimensionality reduction free method. The results indicate that RCEM can improve the efficiency of image retrieval without decreasing the retrieval accuracy, and it can be effectively applied to Content-Based Image Retrieval (CBIR).

Key words: Principal Component Analysis (PCA), Cosine Metric (CM), Euclidean distance, local information, Content-Based Image Retrieval (CBIR)