Fast indexing method for image retrieval using k nearest neighbors searches by principal axis analysis

This paper presents a fast indexing scheme for content-based image retrieval based on the principal axis analysis. Image databases often represent the image objects as high-dimensional feature vectors and access them via the feature vectors and similarity measure. A similarity measure similar to the quadratic histogram distance measure is defined for this indexing method. The computational complexity of similarity measure in high-dimensional image database is very huge and hence the applications of image retrieval are restricted to certain areas. In this work, feature vectors in a given image are ordered by the principal axis analysis to speed up the similarity search in a high-dimensional image database using k nearest neighbor searches. To demonstrate the effectiveness of the proposed algorithm, we conducted extensive experiments and compared the performance with the IBM’s query by image content (QBIC) method, Jain and Vailay’s method, and the LPC-file method. The experimental results demonstrate that the proposed method outperforms the compared methods in retrieval accuracy and execution speed. The execution speed of the proposed method is much faster than that of QBIC method and it can achieve good results in terms of retrieval accuracy compared with Jain’s method and QBIC method.