A review on automatic image annotation techniques

- Zhang, Dengsheng, Islam, Md, Lu, Guojun

  • Authors: Zhang, Dengsheng , Islam, Md , Lu, Guojun
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Pattern Recognition Letters Vol. 45, no. 1 (2012), p. 346-362
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  • Description: Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions

Region based color image retrieval using curvelet transform

- Islam, Md, Zhang, Dengsheng, Lu, Guojun

  • 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
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  • 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

- Islam, Md, Zhang, Dengsheng, Lu, Guojun

  • 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
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  • 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.
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