2011
1.
Rahman, M M; Antani, S K; Thoma, G R
A query expansion framework in image retrieval domain based on local and global analysis Journal Article
In: Information Processing & Management, vol. 47, no. 5, pp. 676-691, 2011, ISSN: 0306-4573, (Managing and Mining Multilingual Documents).
Abstract | Links | BibTeX | Tags: Image retrieval, Query expansion, Relevance feedback, Support vector machine, Vector space model
@article{RAHMAN2011676,
title = {A query expansion framework in image retrieval domain based on local and global analysis},
author = {M M Rahman and S K Antani and G R Thoma},
url = {https://www.sciencedirect.com/science/article/pii/S0306457310001020},
doi = {https://doi.org/10.1016/j.ipm.2010.12.001},
issn = {0306-4573},
year = {2011},
date = {2011-01-01},
journal = {Information Processing & Management},
volume = {47},
number = {5},
pages = {676-691},
abstract = {We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as “bag of concepts” that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall.},
note = {Managing and Mining Multilingual Documents},
keywords = {Image retrieval, Query expansion, Relevance feedback, Support vector machine, Vector space model},
pubstate = {published},
tppubtype = {article}
}
We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as “bag of concepts” that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall.
2009
2.
Rahman, Md. Mahmudur; Bhattacharya, Prabir; Desai, Bipin C
A unified image retrieval framework on local visual and semantic concept-based feature spaces Journal Article
In: Journal of Visual Communication and Image Representation, vol. 20, no. 7, pp. 450-462, 2009, ISSN: 1047-3203.
Abstract | Links | BibTeX | Tags: Classification, Content-based image retrieval, Learning methods, Relevance feedback, Self-organizing map, Similarity fusion, Support vector machine
@article{RAHMAN2009450,
title = {A unified image retrieval framework on local visual and semantic concept-based feature spaces},
author = {Md. Mahmudur Rahman and Prabir Bhattacharya and Bipin C Desai},
url = {https://www.sciencedirect.com/science/article/pii/S1047320309000686},
doi = {https://doi.org/10.1016/j.jvcir.2009.06.001},
issn = {1047-3203},
year = {2009},
date = {2009-01-01},
journal = {Journal of Visual Communication and Image Representation},
volume = {20},
number = {7},
pages = {450-462},
abstract = {This paper presents a learning-based unified image retrieval framework to represent images in local visual and semantic concept-based feature spaces. In this framework, a visual concept vocabulary (codebook) is automatically constructed by utilizing self-organizing map (SOM) and statistical models are built for local semantic concepts using probabilistic multi-class support vector machine (SVM). Based on these constructions, the images are represented in correlation and spatial relationship-enhanced concept feature spaces by exploiting the topology preserving local neighborhood structure of the codebook, local concept correlation statistics, and spatial relationships in individual encoded images. Finally, the features are unified by a dynamically weighted linear combination of similarity matching scheme based on the relevance feedback information. The feature weights are calculated by considering both the precision and the rank order information of the top retrieved relevant images of each representation, which adapts itself to individual searches to produce effective results. The experimental results on a photographic database of natural scenes and a bio-medical database of different imaging modalities and body parts demonstrate the effectiveness of the proposed framework.},
keywords = {Classification, Content-based image retrieval, Learning methods, Relevance feedback, Self-organizing map, Similarity fusion, Support vector machine},
pubstate = {published},
tppubtype = {article}
}
This paper presents a learning-based unified image retrieval framework to represent images in local visual and semantic concept-based feature spaces. In this framework, a visual concept vocabulary (codebook) is automatically constructed by utilizing self-organizing map (SOM) and statistical models are built for local semantic concepts using probabilistic multi-class support vector machine (SVM). Based on these constructions, the images are represented in correlation and spatial relationship-enhanced concept feature spaces by exploiting the topology preserving local neighborhood structure of the codebook, local concept correlation statistics, and spatial relationships in individual encoded images. Finally, the features are unified by a dynamically weighted linear combination of similarity matching scheme based on the relevance feedback information. The feature weights are calculated by considering both the precision and the rank order information of the top retrieved relevant images of each representation, which adapts itself to individual searches to produce effective results. The experimental results on a photographic database of natural scenes and a bio-medical database of different imaging modalities and body parts demonstrate the effectiveness of the proposed framework.