GDL

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

2012
Journal from gdlhub / 2012-06-22 16:58:33
By : P.Jayaprabha, Rm.Somasundaram , STIKOM Dinamika Bangsa Jambi
Created : 2012-06-22, with 1 files

Keyword : CBIR, Multimedia information systems, image retrieval, relevance feedback Image feature extraction, Image analysis, Image search, Image similarity
Subject : Content Based Image Retrieval Methods Using Graphical Image Retrieval Algorithm (GIRA)
Url : http://Integrating Virtual Worlds and Virtual Learning Environments in Schools in Developing Economies
Document Source : Internet

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.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.

Description Alternative :

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.

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