Color image retrieval using statistical model and radial basis function neu
This paper proposes a new and effective framework for color image retrieval based on Full Range Autoregressive Model (FRAR). Bayesian approach (BA) is used to estimate the parameters of the FRAR model. The color autocorrelogram, a new version of edge histogram descriptor (EHD) and micro-texture (MT)...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2014-03-01
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| Series: | Egyptian Informatics Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866514000048 |
| _version_ | 1818570092763414528 |
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| author | K. Seetharaman S. Sathiamoorthy |
| author_facet | K. Seetharaman S. Sathiamoorthy |
| author_sort | K. Seetharaman |
| collection | DOAJ |
| description | This paper proposes a new and effective framework for color image retrieval based on Full Range Autoregressive Model (FRAR). Bayesian approach (BA) is used to estimate the parameters of the FRAR model. The color autocorrelogram, a new version of edge histogram descriptor (EHD) and micro-texture (MT) features are extracted using a common framework based on the FRAR model with BA. The extracted features are combined to form a feature vector, which is normalized and stored in image feature vector database. The feature vector database is categorized according to the nature of the images using the radial basis function neural network (RBFNN) and k-means clustering algorithm. The proposed system adopted Manhattan distance measure of order one to measure the similarity between the query and target images in the categorized and indexed feature vector database. The query refinement approach of short-term learning based relevance feedback mechanism is adopted to reduce the semantic gap. The experimental results, based on precision and recall method are reported. It demonstrates the performance of the improved EHD, effectiveness and efficiency achieved by the proposed framework. |
| first_indexed | 2024-12-14T06:56:20Z |
| format | Article |
| id | doaj.art-61811fabdb574f0ea9b62238bda7be22 |
| institution | Directory Open Access Journal |
| issn | 1110-8665 |
| language | English |
| last_indexed | 2024-12-14T06:56:20Z |
| publishDate | 2014-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj.art-61811fabdb574f0ea9b62238bda7be222022-12-21T23:12:38ZengElsevierEgyptian Informatics Journal1110-86652014-03-01151596810.1016/j.eij.2014.02.001Color image retrieval using statistical model and radial basis function neuK. SeetharamanS. SathiamoorthyThis paper proposes a new and effective framework for color image retrieval based on Full Range Autoregressive Model (FRAR). Bayesian approach (BA) is used to estimate the parameters of the FRAR model. The color autocorrelogram, a new version of edge histogram descriptor (EHD) and micro-texture (MT) features are extracted using a common framework based on the FRAR model with BA. The extracted features are combined to form a feature vector, which is normalized and stored in image feature vector database. The feature vector database is categorized according to the nature of the images using the radial basis function neural network (RBFNN) and k-means clustering algorithm. The proposed system adopted Manhattan distance measure of order one to measure the similarity between the query and target images in the categorized and indexed feature vector database. The query refinement approach of short-term learning based relevance feedback mechanism is adopted to reduce the semantic gap. The experimental results, based on precision and recall method are reported. It demonstrates the performance of the improved EHD, effectiveness and efficiency achieved by the proposed framework.http://www.sciencedirect.com/science/article/pii/S1110866514000048Full range auto regressive modelRadial basis function neural networkColor autocorrelogramEdge histogram descriptorMicro-texture |
| spellingShingle | K. Seetharaman S. Sathiamoorthy Color image retrieval using statistical model and radial basis function neu Egyptian Informatics Journal Full range auto regressive model Radial basis function neural network Color autocorrelogram Edge histogram descriptor Micro-texture |
| title | Color image retrieval using statistical model and radial basis function neu |
| title_full | Color image retrieval using statistical model and radial basis function neu |
| title_fullStr | Color image retrieval using statistical model and radial basis function neu |
| title_full_unstemmed | Color image retrieval using statistical model and radial basis function neu |
| title_short | Color image retrieval using statistical model and radial basis function neu |
| title_sort | color image retrieval using statistical model and radial basis function neu |
| topic | Full range auto regressive model Radial basis function neural network Color autocorrelogram Edge histogram descriptor Micro-texture |
| url | http://www.sciencedirect.com/science/article/pii/S1110866514000048 |
| work_keys_str_mv | AT kseetharaman colorimageretrievalusingstatisticalmodelandradialbasisfunctionneu AT ssathiamoorthy colorimageretrievalusingstatisticalmodelandradialbasisfunctionneu |