List of Titles

Structural image retrieval using automatic image annotation and region based inverted file

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

Authors: Zhang, Dengsheng , Islam, Md , Lu, Guojun
Date: 2013
Type: Text , Journal article
Relation: Journal of Visual Communication and Image Representation Vol. 24, no. 7 (2013), p. 1087-1098
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Description: Image retrieval has lagged far behind text retrieval despite more than two decades of intensive research effort. Most of the research on image retrieval in the last two decades are on content based image retrieval or image retrieval based on low level features. Recent research in this area focuses on semantic image retrieval using automatic image annotation. Most semantic image retrieval techniques in literature, however, treat an image as a bag of features/words while ignore the structural or spatial information in the image. In this paper, we propose a structural image retrieval method based on automatic image annotation and region based inverted file. In the proposed system, regions in an image are treated the same way as keywords in a structural text document, semantic concepts are learnt from image data to label image regions as keywords and weight is assigned to each keyword according to spatial position and relationship. As the result, images are indexed and retrieved in the same way as structural document retrieval. Specifically, images are broken down to regions which are represented using colour, texture and shape features. Region features are then quantized to create visual dictionaries which are similar to monolingual dictionaries like English or Chinese dictionaries. In the next step, a semantic dictionary similar to a bilingual dictionary like the English–Chinese dictionary is learnt to mapping image regions to semantic concepts. Finally, images are then indexed and retrieved using a novel region based inverted file data structure. Results show the proposed method has significant advantage over the widely used Bayesian annotation models.

Rotation invariant curvelet features for region based image retrieval

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

Authors: Zhang, Dengsheng , Islam, Md , Lu, Guojun , Sumana, Ishrat
Date: 2011
Type: Text , Journal article
Relation: International Journal of Computer Vision Vol. 98, no. 2 (2011), p. 187-201
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Reviewed:
Description: There have been much interest and a large amount of research on content based image retrieval (CBIR) in recent years due to the ever increasing number of digital images. Texture features play a key role in CBIR. Many texture features exist in literature, however, most of them are neither rotation invariant nor robust to scale and other variations. Texture features based on Gabor filters have been shown with significant advantages over other methods, and they are adopted by MPEG-7 as one of the texture descriptors for image retrieval. In this paper, we propose a rotation invariant curvelet features for texture representation. With systematic analysis and rigorous experiments, we show that the proposed curvelet texture features significantly outperforms the widely used Gabor texture features. A novel region padding method is also proposed to apply curvelet transform to region based image retrieval. Retrieval results from standard image databases show that curvelet features are promising for both texture and region representation.

Semantic image retrieval using region based inverted file

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

Authors: Zhang, Dengsheng , Islam, Md , Lu, Guojun , Hou, Jin
Date: 2009
Type: Text , Journal article
Relation: Journal of Visual Communication and Image Representation Vol. 24, no. 7 (2009), p.242-249
Full Text: false
Reviewed:
Description: Image retrieval has lagged far behind text retrieval despite more than two decades of intensive research effort. Most of the research on image retrieval in the last two decades are on content based image retrieval or image retrieval based on low level features. Recent research in this area focuses on semantic image retrieval using automatic image annotation. Most semantic image retrieval techniques in literature, however, treat an image as a bag of features/words while ignore the structural or spatial information in the image. In this paper, we propose a structural image retrieval method based on automatic image annotation and region based inverted file. In the proposed system, regions in an image are treated the same way as keywords in a structural text document, semantic concepts are learnt from image data to label image regions as keywords and weight is assigned to each keyword according to spatial position and relationship. As the result, images are indexed and retrieved in the same way as structural document retrieval. Specifically, images are broken down to regions which are represented using colour, texture and shape features. Region features are then quantized to create visual dictionaries which are similar to monolingual dictionaries like English or Chinese dictionaries. In the next step, a semantic dictionary similar to a bilingual dictionary like the English–Chinese dictionary is learnt to mapping image regions to semantic concepts. Finally, images are then indexed and retrieved using a novel region based inverted file data structure. Results show the proposed method has significant advantage over the widely used Bayesian annotation models.

Rotation invariant curvelet features for texture image retrieval

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

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

- 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.

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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Reviewed:
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

Automatic categorization of image regions using dominant color based vector quantization

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

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

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

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.

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
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 memetic approach to protein structure prediction in triangular lattices

- Islam, Md, Chetty, Madhu, Ullah, Abu, Steinhofel, Kathleen

Authors: Islam, Md , Chetty, Madhu , Ullah, Abu , Steinhofel, Kathleen
Date: 2011
Type: Text , Conference paper
Relation: 18th International Conference on Neural Information Processing, ICONIP 2011; Shanghai; China; 13- 17th November 2011; published in Neural Information Processing, (Lecture Notes in Computer Science series) Vol. 7062 p. 625-635
Full Text: false
Reviewed:
Description: Protein structure prediction (PSP) remains one of the most challenging open problems in structural bioinformatics. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this combinatorial optimization problem. In this paper, we describe a clustered meme-based evolutionary approach for PSP using triangular lattice model. Under the framework of memetic algorithm, the proposed method extracts a pool of cultural information from different regions of the search space using data clustering technique. These highly observed local substructures, termed as meme, are then aggregated centrally for further refinements as second stage of evolution. The optimal utilization of 'explore-and-exploit' feature of evolutionary algorithms is ensured by the inherent parallel architecture of the algorithm and subsequent use of cultural information.

Conflict resolution based global search operators for long protein structures prediction

- Islam, Md, Chetty, Madhu, Murshed, Manzur

Authors: Islam, Md , Chetty, Madhu , Murshed, Manzur
Date: 2011
Type: Text , Conference paper
Relation: 18th International Conference on Neural Information Processing, ICONIP 2011; Shanghai; China; 13th to 17th November 2011; published in Neural Information Processing, (Lecture Notes in Computer Science series) Vol. 7062 (1) p.636-645
Full Text: false
Reviewed:
Description: Most population based evolutionary algorithms (EAs) have struggled to accurately predict structure for long protein sequences. This is because conventional operators, i.e., crossover and mutation, cannot satisfy constraints (e.g., connected chain and self-avoiding-walk) of the complex combinatorial multi-modal problem, protein structure prediction (PSP). In this paper, we present novel crossover and mutation operators based on conflict resolution for handling long protein sequences in PSP using lattice models. To our knowledge, this is a pioneering work to address the PSP limitations for long sequences. Experiments carried out with long PDB sequences show the effectiveness of the proposed method. © 2011 Springer-Verlag.

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