Content Based Image Retrieval Methods Using Graphical Image Retrieval Algorithm (GIRA)

This document gives a brief description of a system developed for retrieving images similar to a query image from a large set of distinct images. It follows an image segmentation based approach to extract the different features present in an image. These features are stored in vectors called feature vectors and compared to the feature vectors of query image and thus, the image database is sorted in decreasing order of similarity. Different from traditional dimensionality reduction algorithms such as Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA), which effectively see only the global Euclidean structure, GIRA is designed for discovering the local manifold structure. Therefore, GIRA is likely to be more suitable for image retrieval, where nearest neighbor search is usually involved. After projecting the images into a lower dimensional subspace, the relevant images get closer to the query image; thus, the retrieval performance can be enhanced.