Region based color image retrieval using curvelet transform
- Authors: Islam, Md , Zhang, Dengsheng , Lu, Guojun
- Date: 2010
- Type: Text , Conference paper
- Relation: Proceedings of the 9th Asian Conference on Computer Vision p. 448-457
- Full Text: false
- Reviewed:
- Description: Effective texture feature is an essential component in any content based image retrieval system. In the past, spectral features, like Gabor and wavelet, have shown superior retrieval performance than many other statistical and structural based features. Recent researches on multi-resolution analysis have found that curvelet captures texture properties, like curves, lines, and edges, more accurately than Gabor filters. However, the texture feature extracted using curvelet transform is not rotation invariant. This can degrade its retrieval performance significantly, especially in cases where there are many similar images with different orientations. This paper analyses the curvelet transform and derives a useful approach to extract rotation invariant curvelet features. Experimental results show that the new rotation invariant curvelet feature outperforms the curvelet feature without rotation invariance.
Rotation invariant curvelet features for texture image retrieval
- Authors: Islam, Md , Zhang, Dengsheng , Lu, Guojun
- Date: 2009
- Type: Text , Conference paper
- Relation: Proceedings of the 2009 IEEE International Conference on Multimedia and Expo p. 562-565
- Full Text: false
- Reviewed:
- Description: Effective texture feature is an essential component in any content based image retrieval system. In the past, spectral features, like Gabor and wavelet, have shown superior retrieval performance than many other statistical and structural based features. Recent researches on multi-resolution analysis have found that curvelet captures texture properties, like curves, lines, and edges, more accurately than Gabor filters. However, the texture feature extracted using curvelet transform is not rotation invariant. This can degrade its retrieval performance significantly, especially in cases where there are many similar images with different orientations. This paper analyses the curvelet transform and derives a useful approach to extract rotation invariant curvelet features. Experimental results show that the new rotation invariant curvelet feature outperforms the curvelet feature without rotation invariance.
A geometric method to compute directionality features for texture images
- Authors: Islam, Md , Zhang, Dengsheng , Lu, Guojun
- Date: 2008
- Type: Text , Conference paper
- Relation: Proceedings of the 2008 IEEE International Conference on Multimedia and Expo p. 1521-1524
- Full Text: false
- Reviewed:
- Description: In content based image analysis and retrieval, texture feature is an essential component due to its strong discriminative power. Directionality is one of the most significant texture features which are well perceived by the human visual system. A new method to calculate the directionality of image is proposed in this paper. In contrast to Tamura method which uses the statistical property of the directional histogram of an image to calculate its directionality, the proposed method makes use of the geometric property of the directional histogram. Both subjective and objective analyses prove that the proposed method outperforms the conventional Tamura method. It has also been shown that the proposed directionality has better retrieval performance than the conventional Tamura directionality.
Automatic categorization of image regions using dominant color based vector quantization
- Authors: Islam, Md , Zhang, Dengsheng , Lu, Guojun
- Date: 2008
- Type: Text , Conference paper
- Relation: Proceedings of the Digital Image Computing: Techniques and Applications p. 191-198
- Full Text: false
- Reviewed:
- Description: This paper proposes a dominant color based vector quantization algorithm that automatically categorizes image regions. In contrast to the conventional vector quantization algorithm, the new algorithm effectively handles variable feature vectors like dominant color descriptors. Furthermore, the algorithm is guided by a novel splitting and stopping criterion which is specially designed for dominant color descriptors. This criterion helps the algorithm not only to learn the number of clusters, but also to avoid unnecessary over-fragmentations of region-clusters. Experimental result shows that the proposed approach categorizes image-regions with very high accuracy.
Content based image retrieval using curvelet transform
- Authors: Sumana, Ishrat , Islam, Md , Zhang, Dengsheng , Lu, Guojun
- Date: 2008
- Type: Text , Conference paper
- Relation: Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing p. 11-16
- Full Text: false
- Reviewed:
- Description: Feature extraction is a key issue in content-based image retrieval (CBIR). In the past, a number of texture features have been proposed in literature, including statistic methods and spectral methods. However, most of them are not able to accurately capture the edge information which is the most important texture feature in an image. Recent researches on multi-scale analysis, especially the curvelet research, provide good opportunity to extract more accurate texture feature for image retrieval. Curvelet was originally proposed for image denoising and has shown promising performance. In this paper, a new image feature based on curvelet transform has been proposed. We apply discrete curvelet transform on texture images and compute the low order statistics from the transformed images. Images are then represented using the extracted texture features. Retrieval results show, it significantly outperforms the widely used Gabor texture feature.