An Efficient Index Structure for High Dimensional Image Data

1999 ◽  
pp. 131-144 ◽  
Author(s):  
Jae Soo Yoo ◽  
Myung Keun Shin ◽  
Seok Hee Lee ◽  
Kil Seong Choi ◽  
Ki Hyung Cho ◽  
...  
Keyword(s):  
Image Data ◽  
Index Structure ◽  
High Dimensional ◽  
Dimensional Image

Feature Extraction in Remote Sensing High-Dimensional Image Data

2007 ◽  
Vol 4 (1) ◽  
pp. 107-111 ◽  
Author(s):  
Maciel Zortea ◽  
Victor Haertel ◽  
Robin Clarke
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Image Data ◽  
High Dimensional ◽  
Dimensional Image

scholarly journals An Efficient Content-Based High-Dimensional Index Structure for Image Data

ETRI Journal ◽  
2000 ◽  
Vol 22 (2) ◽  
pp. 32-42
Author(s):  
Jang Sun Lee Lee ◽  
Jae Soo Yoo Yoo ◽  
Seok Hee Lee Lee ◽  
Myung-Joon Kim Kim
Keyword(s):  
Image Data ◽  
Index Structure ◽  
High Dimensional

High-Dimensional Image Data Sets Retrieval: Improving Accuracy Using a Weighted Relevance Feedback

2015 ◽  
Vol 09 (02) ◽  
pp. 239-259
Author(s):  
Abir Gallas ◽  
Walid Barhoumi ◽  
Ezzeddine Zagrouba
Keyword(s):  
Relevance Feedback ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Image Data ◽  
High Dimensional ◽  
General Context ◽  
Query Image ◽  
Data Set ◽  
Dimensional Image ◽  
Minimum Number

The user's interaction with the retrieval engines, while seeking a particular image (or set of images) in large-scale databases, defines better his request. This interaction is essentially provided by a relevance feedback step. In fact, the semantic gap is increasing in a remarkable way due to the application of approximate nearest neighbor (ANN) algorithms aiming at resolving the curse of dimensionality. Therefore, an additional step of relevance feedback is necessary in order to get closer to the user's expectations in the next few retrieval iterations. In this context, this paper details a classification of the different relevance feedback techniques related to region-based image retrieval applications. Moreover, a technique of relevance feedback based on re-weighting regions of the query-image by selecting a set of negative examples is elaborated. Furthermore, the general context to carry out this technique which is the large-scale heterogeneous image collections indexing and retrieval is presented. In fact, the main contribution of the proposed work is affording efficient results with the minimum number of relevance feedback iterations for high dimensional image databases. Experiments and assessments are carried out within an RBIR system for "Wang" data set in order to prove the effectiveness of the proposed approaches.

Image Characteristics Indexing Based on X-Tree

2014 ◽  
Vol 513-517 ◽  
pp. 3761-3764
Author(s):  
Rong Hua Gao ◽  
Hua Rui Wu
Keyword(s):  
Image Data ◽  
Index Structure ◽  
High Dimensional ◽  
Data Set ◽  
Image Objects ◽  
Image Characteristics ◽  
Retrieval Efficiency ◽  
Feature Based ◽  
Retrieval Result ◽  
Indexing Technique

Image data set are usually very large, which might consist of millions of image objects, it is essential to use an efficient and effective indexing technique to facilitate speedy searching. The features can be expressed in terms of high-dimensional vector data which can be compared with a given query for similarity between them. It is more important that the image database should be preprocessed and establish indexing to improve retrieval efficiency. In this paper, the method of improved X-tree is proposed, design and implementation of a high dimensional index application to facilitate the speedy searching in feature based image information retrieval. Compared by retrieval efficiency and retrieval result, it is convincingly proved that hierarchical index structure based on clustering is efficient and applicable in image characteristics indexing.

scholarly journals Estabilidade de Classificadores de Decisão em Árvore Binária para Dados Imagem em Alta Dimensão

2008 ◽  
Vol 15 (2) ◽  
pp. 27-42
Author(s):  
Denis Altieri De Oliveira Moraes ◽  
Victor Haertel
Keyword(s):  
Binary Tree ◽  
Image Data ◽  
Computer Time ◽  
Covariance Matrices ◽  
Feature Reduction ◽  
High Dimensional ◽  
Bhattacharyya Distance ◽  
Dimensional Image ◽  
Direct Use ◽  
Selection Of

This paper deals with the problem of classifying high-dimensional image data image data using a multiple stage classifier structured as a binary tree. The aim here consists in finding the optimal structure for the binary tree in the sense of achieving a stable accuracy. The advantage presented by a multiple stage classifier lies on the fact that only a sub-set of classes is considered at each stage, allowing a better selection of the features to be used at each node.  The binary tree is a particular case of a tree structured classifier, on which only two classes are considered at each node. This peculiarity makes possible the direct use of statistical distances for feature reduction (selection or extraction). In this study the criterion used for feature reduction at each node consists in optimizing the Bhattacharyya distance separating both classes in the node. The optimization of Bhattacharyya distance was based on the covariance matrices. Once the final set of features is obtained at each particular node, the classification is performed using the Gaussian Maximum Likelihood decision rule. Tests were performed using high-dimensional image data collected by the sensor system AVIRIS covering a test area. The criteria to evaluate the performance of the classifiers are: the final accuracy yielded by the classifier, its stability, and the required computer time.

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