List of Titles

Automatic building footprint extraction and regularisation from LIDAR point cloud data

- Awrangjeb, Mohammad, Lu, Guojun

Authors: Awrangjeb, Mohammad , Lu, Guojun
Date: 2014
Type: Text , Conference proceedings
Full Text: false
Description: This paper presents a segmentation of LIDAR point cloud data for automatic extraction of building footprint. Using the ground height information from a DEM (Digital Elevation Model), the non-ground points (mainly buildings and trees) are separated from the ground points. Points on walls are removed from the set of non-ground points. The remaining non-ground points are then divided into clusters based on height and local neighbourhood. Planar roof segments are extracted from each cluster of points following a region-growing technique. Planes are initialised using coplanar points as seed points and then grown using plane compatibility tests. Once all the planar segments are extracted, a rule-based procedure is applied to remove tree planes which are small in size and randomly oriented. The neighbouring planes are then merged to obtain individual building boundaries, which are regularised based on a new feature-based technique. Corners and line-segments are extracted from each boundary and adjusted using the assumption that each short building side is parallel or perpendicular to one or more neighbouring long building sides. Experimental results on five Australian data sets show that the proposed method offers higher correctness rate in building footprint extraction than a state-of-the-art method.

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
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 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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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.

On low-rank regularized least squares for scalable nonlinear classification

- Fu, Zhouyu, Lu, Guojun, Ting, Kaiming, Zhang, Dengsheng

Authors: Fu, Zhouyu , Lu, Guojun , Ting, Kaiming , Zhang, Dengsheng
Date: 2011
Type: Text , Conference paper
Relation: International Conference on Neural Information Processing p. 490-499
Full Text: false
Reviewed:
Description: In this paper, we revisited the classical technique of Regularized Least Squares (RLS) for the classification of large-scale nonlinear data. Specifically, we focus on a low-rank formulation of RLS and show that it has linear time complexity in the data size only and does not rely on the number of labels and features for problems with moderate feature dimension. This makes low-rank RLS particularly suitable for classification with large data sets. Moreover, we have proposed a general theorem for the closed-form solutions to the Leave-One-Out Cross Validation (LOOCV) estimation problem in empirical risk minimization which encompasses all types of RLS classifiers as special cases. This eliminates the reliance on cross validation, a computationally expensive process for parameter selection, and greatly accelerate the training process of RLS classifiers. Experimental results on real and synthetic large-scale benchmark data sets have shown that low-rank RLS achieves comparable classification performance while being much more efficient than standard kernel SVM for nonlinear classification. The improvement in efficiency is more evident for data sets with higher dimensions.

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Multimodal image registration technique based on improved local feature descriptors

- Teng, Shyh, Hossain, Tanvir, Lu, Guojun

Authors: Teng, Shyh , Hossain, Tanvir , Lu, Guojun
Date: 2015
Type: Text , Journal article
Relation: Journal of Electronic Imaging Vol. 24, no. 1 (2015), p.
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Description: Multimodal image registration has received significant research attention over the past decade, and the majority of the techniques are global in nature. Although local techniques are widely used for general image registration, there are only limited studies on them for multimodal image registration. Scale invariant feature transform (SIFT) is a well-known general image registration technique. However, SIFT descriptors are not invariant to multimodality. We propose a SIFT-based technique that is modality invariant and still retains the strengths of local techniques. Moreover, our proposed histogram weighting strategies also improve the accuracy of descriptor matching, which is an important image registration step. As a result, our proposed strategies can not only improve the multimodal registration accuracy but also have the potential to improve the performance of all SIFT-based applications, e.g., general image registration and object recognition.

Efficient nonlinear classification via low-rank regularised least squares

- Fu, Zhouyu, Lu, Guojun, Ting, Kaiming, Zhang, Dengsheng

Authors: Fu, Zhouyu , Lu, Guojun , Ting, Kaiming , Zhang, Dengsheng
Date: 2013
Type: Text , Journal article
Relation: Neural Computing and Applications Vol. 22, no. 7-8(2013), p. 1279-1289
Full Text: false
Reviewed:
Description: We revisit the classical technique of regularised least squares (RLS) for nonlinear classification in this paper. Specifically, we focus on a low-rank formulation of the RLS, which has linear time complexity in the size of data set only, independent of both the number of classes and number of features. This makes low-rank RLS particularly suitable for problems with large data and moderate feature dimensions. Moreover, we have proposed a general theorem for obtaining the closed-form estimation of prediction values on a holdout validation set given the low-rank RLS classifier trained on the whole training data. It is thus possible to obtain an error estimate for each parameter setting without retraining and greatly accelerate the process of cross-validation for parameter selection. Experimental results on several large-scale benchmark data sets have shown that low-rank RLS achieves comparable classification performance while being much more efficient than standard kernel SVM for nonlinear classification. The improvement in efficiency is more evident for data sets with higher dimensions.

Learning naive Bayes classifiers for music classification and retrieval

- Fu, Zhouyu, Lu, Guojun, Ting, Kaiming, Zhang, Dengsheng

Authors: Fu, Zhouyu , Lu, Guojun , Ting, Kaiming , Zhang, Dengsheng
Date: 2010
Type: Text , Conference paper
Relation: Proceedings of the 20th International Conference on Pattern Recognition p. 4589-4592
Full Text: false
Reviewed:
Description: In this paper, we explore the use of naive Bayes classifiers for music classification and retrieval. The motivation is to employ all audio features extracted from local windows for classification instead of just using a single song-level feature vector produced by compressing the local features. Two variants of naive Bayes classifiers are studied based on the extensions of standard nearest neighbor and support vector machine classifiers. Experimental results have demonstrated superior performance achieved by the proposed naive Bayes classifiers for both music classification and retrieval as compared to the alternative methods.

Effective and efficient contour-based corner detectors

- Teng, Shyh, Najmus Sadat, Rafi, Lu, Guojun

Authors: Teng, Shyh , Najmus Sadat, Rafi , Lu, Guojun
Date: 2015
Type: Text , Journal article
Relation: Pattern Recognition Vol. 48, no. 7 (2015), p. 2185-2197
Full Text: false
Reviewed:
Description: Corner detection is an essential operation in many computer vision applications. Among the contour-based corner detectors in the literature, the Chord-to-Point Distance Accumulation (CPDA) detector is reported to have one of the highest repeatability in detecting robust corners and the lowest localization error. However, based on our analysis, we found that the CPDA detector often fails to accurately detect the true corners when a curve has multiple corners but the sharpness of one or a few of them is much more prominent than the rest. This detector also might not perform well when the corners are closely located. Furthermore, the CPDA detector is also computationally very expensive. To overcome these weaknesses, we propose two effective and efficient corner detectors using simple triangular theory and distance calculation. Our experimental results show that our proposed detectors outperform CPDA and nine other existing corner detectors in terms of repeatability. Our proposed detectors also have a relatively low or comparable localization error and are computationally more efficient. © 2015 Elsevier Ltd.

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.

Spherical harmonics and distance transform for image representation and retrieval

- Sajjanhar, Atul, Lu, Guojun, Zhang, Dengsheng, Hou, Jingyu, Chen, Yi-Ping Phoebe

Authors: Sajjanhar, Atul , Lu, Guojun , Zhang, Dengsheng , Hou, Jingyu , Chen, Yi-Ping Phoebe
Date: 2009
Type: Text , Conference paper
Relation: Proceedings of the Intelligent Data Engineering and Automated Learning p. 309-316
Full Text: false
Reviewed:
Description: In this paper, we have proposed a method for 2D image retrieval based on object shapes. The method relies on transforming the 2D images into 3D space based on distance transform. Spherical harmonics are obtained for the 3D data and used as descriptors for the underlying 2D images. The proposed method is compared against two existing methods which use spherical harmonics for shape based retrieval of images. MPEG-7 Still Images Content Set is used for performing experiments; this dataset consists of 3621 still images. Experimental results show that the performance of the proposed descriptors is significantly better than other methods in the same category.

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.

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Fusion of LiDAR data and multispectral imagery for effective building detection based on graph and connected component analysis

- Gilani, Alinaqi, Awrangjeb, Mohammad, Lu, Guojun

Authors: Gilani, Alinaqi , Awrangjeb, Mohammad , Lu, Guojun
Date: 2015
Type: Text , Conference proceedings
Full Text:
Description: Building detection in complex scenes is a non-trivial exercise due to building shape variability, irregular terrain, shadows, and occlusion by highly dense vegetation. In this research, we present a graph based algorithm, which combines multispectral imagery and airborne LiDAR information to completely delineate the building boundaries in urban and densely vegetated area. In the first phase, LiDAR data is divided into two groups: ground and non-ground data, using ground height from a bare-earth DEM. A mask, known as the primary building mask, is generated from the non-ground LiDAR points where the black region represents the elevated area (buildings and trees), while the white region describes the ground (earth). The second phase begins with the process of Connected Component Analysis (CCA) where the number of objects present in the test scene are identified followed by initial boundary detection and labelling. Additionally, a graph from the connected components is generated, where each black pixel corresponds to a node. An edge of a unit distance is defined between a black pixel and a neighbouring black pixel, if any. An edge does not exist from a black pixel to a neighbouring white pixel, if any. This phenomenon produces a disconnected components graph, where each component represents a prospective building or a dense vegetation (a contiguous block of black pixels from the primary mask). In the third phase, a clustering process clusters the segmented lines, extracted from multispectral imagery, around the graph components, if possible. In the fourth step, NDVI, image entropy, and LiDAR data are utilised to discriminate between vegetation, buildings, and isolated building's occluded parts. Finally, the initially extracted building boundary is extended pixel-wise using NDVI, entropy, and LiDAR data to completely delineate the building and to maximise the boundary reach towards building edges. The proposed technique is evaluated using two Australian data sets: Aitkenvale and Hervey Bay, for object-based and pixel-based completeness, correctness, and quality. The proposed technique detects buildings larger than 50 m2 and 10 m2 in the Aitkenvale site with 100% and 91% accuracy, respectively, while in the Hervey Bay site it performs better with 100% accuracy for buildings larger than 10 m2 in area.

A novel multi-modal image registration method based on corners

- Lv, Guohua, Teng, Shyh, Lu, Guojun

Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun
Date: 2015
Type: Text , Conference proceedings
Relation: 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014, Wollongong, New South Wales, 25th-27th November 2014
Full Text: false
Description: This paper presents a novel method for registering multi-modal images, based on corners. The proposed method is motivated by the fact that large content differences are likely to occur in multi-modal images. Unlike traditional multi-modal image registration methods that utilize intensities or gradients for feature representation, we propose to use curvatures of corners. Moreover, a novel local descriptor called Distribution of Edge Pixels Along Contour (DEPAC) is proposed to represent the neighborhood of corners. Curvature and DEPAC similarities are combined in our method to improve the registration accuracy. Using a set of benchmark multi-modal images and multi-modal microscopic images, we demonstrate that our proposed method outperforms an existing state-of-the-art image registration method.

An enhancement to SIFT-based techniques for image registration

- Hossain, Tanvir, Teng, Shyh, Lu, Guojun, Lackmann, Martin

Authors: Hossain, Tanvir , Teng, Shyh , Lu, Guojun , Lackmann, Martin
Date: 2010
Type: Text , Conference paper
Relation: Proceedings of the 2010 Digital Image Computing: Techniques and Applications p. 166-171
Full Text: false
Reviewed:
Description: Symmetric-SIFT is a recently proposed local technique used for registering multimodal images. It is based on a well-known general image registration technique named Scale Invariant Feature Transform (SIFT). Symmetric SIFT makes use of the gradient magnitude information at the image's key regions to build the descriptors. In this paper, we highlight an issue with how the magnitude information is used in this process. This issue may result in similar descriptors being built to represent regions in images that are visually different. To address this issue, we have proposed two new strategies for weighting the descriptors. Our experimental results show that Symmetric-SIFT descriptors built using our proposed strategies can lead to better registration accuracy than descriptors built using the original Symmetric-SIFT technique. The issue highlighted and the two strategies proposed are also applicable to the general SIFT technique.

Connectivity-based shape descriptors

- Sajjanhar, Atul, Lu, Guojun, Zhang, Dengsheng, Zhou, Wanle

Authors: Sajjanhar, Atul , Lu, Guojun , Zhang, Dengsheng , Zhou, Wanle
Date: 2010
Type: Text , Journal article
Relation: International Journal of Computers and Applications Vol. 32, no. 1 (2010), p. 93-98
Full Text: false
Reviewed:
Description: In this paper, we propose a method for indexing and retrieval of images based on shapes of objects. The concept of connectivity is introduced. 3D models are used to represent 2D images. 2D images are decomposed a priori using connectivity which is followed by 3D model construction. 3D model descriptors are obtained for 3D models and used to represent the underlying 2D shapes. We have used spherical harmonics descriptors as the 3D model descriptors. Difference between two images is computed as the Euclidean distance between their descriptors. Experiments are performed to test the effectiveness of spherical harmonics for retrieval of 2D images. The proposed method is compared with methods based on principal components analysis (PCA) and generic Fourier descriptors (GFD). It is found that the proposed method is effective. Item S8 within the MPEG-7 still images content set is used for performing experiments.

Efficient and effective transformed image identification

- Awrangjeb, Mohammad, Lu, Guojun

Authors: Awrangjeb, Mohammad , Lu, Guojun
Date: 2008
Type: Text , Conference proceedings
Full Text: false
Description: The SIFT (scale invariant feature transform) has demonstrated its superior performance in identifying transformed images over many other approaches. However, both of its detection and matching stages are expensive, because a large number of keypoints are detected in the scale-space and each keypoint is described using a 128-dimensional vector. We present two possible solutions for feature-point reduction. First is to down scale the image before the SIFT keypoint detection and second is to use corners (instead of SIFT keypoints) which are visually significant, more robust, and much smaller in number than the SIFT keypoints. Either the curvature descriptor or the highly distinctive SIFT descriptors at corner locations can be used to represent corners.We then describe a new feature-point matching technique, which can be used for matching both the down-scaled SIFT keypoints and corners. Experimental results show that two feature-point reduction solutions combined with the SIFT descriptors and the proposed feature-point matching technique not only improve the computational efficiency and decrease the storage requirement, but also improve the transformed image identification accuracy (robustness).

Music emotion annotation by machine learning

- Cheung, Wai, Lu, Guojun

Authors: Cheung, Wai , Lu, Guojun
Date: 2008
Type: Text , Conference paper
Relation: Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing p. 580-585
Full Text: false
Reviewed:
Description: Music emotion annotation is a task of attaching emotional terms to musical works. As volume of online musical contents expands rapidly in recent years, demands for retrieval by emotion are emerging. Currently, literature on music retrieval using emotional terms is rare. Emotion annotated data are scarce in existing music databases because annotation is still a manual task. Automating music emotion annotation is an essential prerequisite to research in music retrieval by emotion, for without which even sophisticated retrieval methods may not be very useful in a data deficient environment. This paper describes a machine learning approach to annotate music using a large number of emotional terms. We also estimate the training data size requirements for a workable annotation system. Our empirical result shows that 1) the task of music emotion annotation could be modelled using machine learning techniques to support a large number of emotional terms, 2) the combination of sampling method and data-driven detection threshold is highly effective in optimizing the use of existing annotated data in training machine learning models, 3) synonymous relationships enhance the annotation performance and 4) the training data size requirement is within reach for a workable annotation system. Essentially, automatic music emotion annotation enables music retrieval by emotion to be performed as a text retrieval task.

Enhanced polyphonic music genre classification using high level features

- Arabi, Arash, Lu, Guojun

Authors: Arabi, Arash , Lu, Guojun
Date: 2009
Type: Text , Conference paper
Relation: Proceedings of the 2009 IEEE International Conference on Signal and Image Processing Applications p. 1-6
Full Text: false
Reviewed:
Description: The task of classifying the genre of polyphonic music signals is traditionally done using only low level features of the signal. In this paper high level features have been applied to improve the task of music genre classification. The use of statistical chord features and chord progression information in conjunction with low level features are proposed in this paper. The chord progression information is manifested in genre probability descriptors calculated using a pattern matching algorithm. Our proposed method provides an improvement of 12.4% in the classification results over a commonly compared technique.

Image retrieval based on semantics of intra-region color properties

- Sajjanhar, Atul, Lu, Guojun, Zhang, Dengsheng, Zhou, Wanlei, Chen, Yi-Ping Phoebe

Authors: Sajjanhar, Atul , Lu, Guojun , Zhang, Dengsheng , Zhou, Wanlei , Chen, Yi-Ping Phoebe
Date: 2008
Type: Text , Conference paper
Relation: Proceedings of 2008 IEEE 8th International Conference on Computer and Information Technology p. 338-343
Full Text: false
Reviewed:
Description: Traditional image retrieval systems are content based image retrieval systems which rely on low-level features for indexing and retrieval of images. CBIR systems fail to meet user expectations because of the gap between the low level features used by such systems and the high level perception of images by humans. Semantics based methods have been used to describe images according to their high level features. In this paper, we performed experiments to identify the failure of existing semantics-based methods to retrieve images in a particular semantic category. We have proposed a new semantic category to describe the intra-region color feature. The proposed semantic category complements the existing high level descriptions. Experimental results confirm the effectiveness of the proposed method

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