S. Ghanbari and J. C. Woods and S. M. Lucas (2009) Semi-automatic BPT for Image Retrieval. In: 2009 Seventh International Workshop on Content-Based Multimedia Indexing, 2009-06-03 - 2009-06-05.
S. Ghanbari and J. C. Woods and S. M. Lucas (2009) Semi-automatic BPT for Image Retrieval. In: 2009 Seventh International Workshop on Content-Based Multimedia Indexing, 2009-06-03 - 2009-06-05.
S. Ghanbari and J. C. Woods and S. M. Lucas (2009) Semi-automatic BPT for Image Retrieval. In: 2009 Seventh International Workshop on Content-Based Multimedia Indexing, 2009-06-03 - 2009-06-05.
Abstract
This paper presents a novel semi-automatic tool for content retrieval. A multidimension binary partition tree (BPT) is generated to perform object based image retrieval. The tree is colour based but has the advantage of incorporating spatial frequency to form semantically meaningful tree nodes. For retrieval, a node of the query image is matched against the nodes of the BPT of the database image. These are matched according to a combination of colour histograms, texture features and edge histograms. This semi-automatic tool allows users to have more freedom in their choice of query. The paper illustrates how the use of multidimensional information can significantly enhance content retrieval results for natural images.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | Notes: This paper presents a novel semi-automatic tool for content retrieval. A multidimension binary partition tree (BPT) is generated to perform object based image retrieval. The tree is colour based but has the advantage of incorporating spatial frequency to form semantically meaningful tree nodes. For retrieval, a node of the query image is matched against the nodes of the BPT of the database image. These are matched according to a combination of colour histograms, texture features and edge histograms. This semi-automatic tool allows users to have more freedom in their choice of query. The paper illustrates how the use of multidimensional information can significantly enhance content retrieval results for natural images. |
| Uncontrolled Keywords: | content-based retrieval; image colour analysis; image retrieval; image texture; visual databases; colour histograms; database image; edge histograms; image enhancement; multidimension binary partition tree; query image; texture features; tree nodes; Content based retrieval; Data mining; Frequency; Histograms; Humans; Image databases; Image segmentation; Information retrieval; Merging; binary partition tree |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
| SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
| Depositing User: | Unnamed user with email elements@essex.ac.uk |
| Date Deposited: | 11 Oct 2012 13:46 |
| Last Modified: | 30 Oct 2024 19:46 |
| URI: | http://repository.essex.ac.uk/id/eprint/4052 |