Comparison of curvelet and wavelet texture features for content based image retrieval
- Authors: Sumana, Ishrat , Lu, Guojun , Zhang, Dengsheng
- Date: 2012
- Type: Text , Conference paper
- Relation: 2012 IEEE International Conference on Multimedia and Expo (ICME) p. 290-295
- Full Text: false
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- Description: Texture feature plays a vital role in content based Image retrieval (CBIR). Wavelet texture feature modeled by generalized Gaussian density (GGD) [1] performs better than discrete wavelet texture feature. Curve let texture feature was proposed in [2]. In this paper, we compute a new texture feature by applying the generalized Gaussian density to the distribution of curve let coefficients which we call curve let GGD texture feature. The purpose of this paper is to investigate curve let GGD texture feature and compare its retrieval performance with that of curve let, wavelet and wavelet GGD texture features. Experimental results show that both curve let and curve let GGD features perform significantly better than wavelet and wavelet GGD texture features. Among the two types of curve let based features, curve let feature shows better performance in CBIR than curve let GGD texture feature. The findings are discussed in the paper.
Composite feature modeling and retrieval
- Authors: Hou, Jin , Zhang, Dengsheng , Chen, Zeng , Xu, Xuerong , Nakamura, Takahiro
- Date: 2008
- Type: Text , Conference paper
- Relation: Proceedings of the 2008 10th International Conference on Control, Automation, Robotics & Vision p. 2176-2181
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- Description: Feature-based intelligent design and manufacturing systems in the Internet environment are an evolution of traditional geometric and solid modeling systems. This paper presents some novel algorithms including a new face-base representation, composite feature modeling and retrieval technology, and efficient communication mechanism, to construct an interactive framework for composite feature modeling and retrieval. The proposed system consists of a feature modeler developed on Wolfram Research Mathematica, Java and Java 3D enabled GUI (graphical user interface), and DB (database). Experiments demonstrate that this system reflects designers' intent properly and is user-friendly to experts coming from various technical backgrounds. This paper provides some fundamental principles for composite feature modeling and retrieval in web-based distributed environment.
Connectivity-based shape descriptors
- 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
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- 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.
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
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- 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.
Corners-based composite descriptor for shapes
- Authors: Sajjanhar, Atul , Lu, Guojun , Zhang, Dengsheng , Zhou, Wanlei
- Date: 2008
- Type: Text , Conference paper
- Relation: Proceedings of the First International Congress on Image and Signal Processing CISP2008 p. 714-718
- Full Text: false
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- Description: In this paper, a composite descriptor for shape retrieval is proposed. The composite descriptor is obtained based upon corner-points and shape region. In an earlier paper, we proposed a composite descriptor based on shape region and shape contour, however, the descriptor was not effective for all perspective and geometric transformations. Hence, we modify the composite descriptor by replacing contour features with corner-points features. The proposed descriptor is obtained from Generic FourierDescriptors (GFD) of the shape region and the GFD ofthe corner-points. We study the performance of the proposed composite descriptor. The proposed method is evaluated using Item S8 within the MPEG-7 Still Images Content Set. Experimental results show that the proposed descriptor is effective.
Digital image retrieval using intermediate semantic features and multistep search
- Authors: Zhang, Dengsheng , Liu, Ying , Hou, Jin
- Date: 2008
- Type: Text , Conference paper
- Relation: Proceedings of the Digital Image Computing: Techniques and Applications p. 513-518
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- Description: Recently, semantic image retrieval has attracted large amount of interest due to the rapid growth of digital image storage. However, existing approaches have severe limitations. In this paper, a new approach to digital image retrieval using intermediate semantic features and multistep search has been proposed. Instead of looking for human level semantics which is too challenging at this stage, the research looks for heuristic information and intermediate semantic features which can describe image content objectively. Different from the conventional approaches, the intermediate features are used as filters to eliminate large amount of irrelevant images. Conventional content based image retrieval techniques and relevance feedback (RF) are applied following the filtering to improve the retrieval accuracy. The proposed system has the power of capturing both regional features and global features, and making use of both semantic features and low level features. The proposed system also uses a powerful user interface to provide users with convenient retrieval mechanisms including SQL, RF and query by example. Results show the system has a significant gain over existing region based and global image retrieval approaches
Distortion robust image classification using deep convolutional neural network with discrete cosine transform
- Authors: Hossain, Md Tahmid , Teng, Shyh Wei , Zhang, Dengsheng , Lim, Suryani , Lu, Guojun
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 IEEE International Conference on Image Processing (ICIP);Taipei, Taiwan; 22-25 Sept, 2019 p. 659-663
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- Description: Convolutional Neural Networks are highly effective for image classification. However, it is still vulnerable to image distortion. Even a small amount of noise or blur can severely hamper the performance of these CNNs. Most work in the literature strives to mitigate this problem simply by fine-tuning a pre-trained CNN on mutually exclusive or a union set of distorted training data. This iterative fine-tuning process with all known types of distortion is exhaustive and the network struggles to handle unseen distortions. In this work, we propose distortion robust DCT-Net, a Discrete Cosine Transform based module integrated into a deep network which is built on top of VGG16 [1]. Unlike other works in the literature, DCT-Net is "blind" to the distortion type and level in an image both during training and testing. The DCT-Net is trained only once and applied in a more generic situation without further retraining. We also extend the idea of dropout and present a training adaptive version of the same. We evaluate our proposed DCT-Net on a number of benchmark datasets. Our experimental results show that once trained, DCT-Net not only generalizes well to a variety of unseen distortions but also outperforms other comparable networks in the literature.
Efficient nonlinear classification via low-rank regularised least squares
- 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
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- 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.
Enhancing the effectiveness of local descriptor based image matching
- Authors: Hossain, Md Tahmid , Teng, Shyh , Zhang, Dengsheng , Lim, Suryani , Lu, Guojun
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018 p. 1-8
- Full Text: false
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- Description: Image registration has received great attention from researchers over the last few decades. SIFT (Scale Invariant Feature Transform), a local descriptor-based technique is widely used for registering and matching images. To establish correspondences between images, SIFT uses a Euclidean Distance ratio metric. However, this approach leads to a lot of incorrect matches and eliminating these inaccurate matches has been a challenge. Various methods have been proposed attempting to mitigate this problem. In this paper, we propose a scale and orientation harmony-based pruning method that improves image matching process by successfully eliminating incorrect SIFT descriptor matches. Moreover, our technique can predict the image transformation parameters based on a novel adaptive clustering method with much higher matching accuracy. Our experimental results have shown that the proposed method has achieved averages of approximately 16% and 10% higher matching accuracy compared to the traditional SIFT and a contemporary method respectively.
- Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Extracting road centrelines from binary road images by optimizing geodesic lines
- Authors: Zhou, Shaoguang , Lu, Guojun , Teng, Shyh , Zhang, Dengsheng
- Date: 2016
- Type: Text , Conference proceedings , Conference paper
- Relation: 2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015; Auckland, New Zealand; 23rd-24th November 2015 Vol. 2016-November, p. 1-6
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- Description: Binary road images can be obtained from remotely sensed images with the aid of classification and segmentation techniques. Extracting road centrelines from these binary images are crucial to update a Geographic Information System (GIS) database. A current state of art method of centreline extraction needs to remove road junctions and depends on the accuracy of the endpoints, leading to three main limitations: (1) causing small gaps in the roads, (2) wrongly treating short non-road segments as roads, and (3) producing centrelines of low accuracy around the road end regions. To overcome these limitations, we propose to use an iteratively searching scheme to obtain the longest geodesic line in the preprocessed road skeleton images. Several image pixels at each end of the geodesic lines were removed to avoid noise, and the remaining parts were optimized using a dynamic programming snake model. The proposed method is applied to three types of binary road images and compared with the state of art method. It shows that the proposed method is less affected by the end regions of the roads, and is effective in filling the gaps in the roads. It also has an advantage on processing short non-road segments. © 2015 IEEE.
- Description: International Conference Image and Vision Computing New Zealand
Fine-grained image classification based on knowledge distillation
- Authors: Liu, Ying , Feng, Hao , Zhang, Weidong , Fang, Jie , Xiao, Peng , Zhang, Dengsheng
- Date: 2023
- Type: Text , Conference paper
- Relation: 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023, Harbin, China, 29-31 July 2023, 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
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- Description: Despite the outstanding performance of deep learning-based fine-grained image classification methods, the commonly used models still suffer from high cost of computation and memory Therefore, this paper proposes a mobile-based CNN network that focuses on discriminative features of fine-grained images by embedding a hybrid-domain attention module to achieve higher accuracy in recognition. Specifically, under the premise of reducing network parameters, this paper presents a classification method that combines transfer learning and knowledge distillation to enhance the model's generalization performance and resistance to overfitting. Different knowledge transfer strategies are validated through the experiments in the knowledge distillation process. Mobile models such as SqueezeNet, MobileNetV2, and CBAM MobileNetV2 all demonstrate enhanced performance the knowledge distillation optimization. The proposed method in this paper can be used to develop a lightweight mobile-based CNN model with comparable performance to complex models making it more advantageous in real-life scenarios with limited storage resources and low hardware computation levels. Additionally, the model compression process utilizes only the intermediate features of the original dataset, meeting the confidentiality requirements of the original data in the field of public security. © 2023 IEEE.
Image clustering using a similarity measure incorporating human perception
- Authors: Shojanazeri, Hamid , Aryal, Sunil , Teng, Shyh , Zhang, Dengsheng , Lu, Guojun
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 International Conference on Image and Vision Computing New Zealand, IVCNZ 2018; Auckland, New Zealand; 19th-21st November 2018 p. 1-6
- Full Text: false
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- Description: Clustering similar images is an important task in image processing and computer vision. It requires a measure to quantify pairwise similarities of images. The performance of clustering algorithm depends on the choice of similarity measure. In this paper, we investigate the effectiveness of data independent (distance-based), data-dependent (mass-based) and hybrid (dis)similarity measures in the image clustering task using three benchmark image collections with different sets of features. Our results of K-Medoids clustering show that uses the hybrid Perceptual Dissimilarity Measure (PMD) produces better clustering results than distance-based l(p) - norm and mass-based m(p) - dissimilarity.
Image retrieval based on semantics of intra-region color properties
- 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
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- 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
Improved kernel descriptors for effective and efficient image classification
- Authors: Karmakar, Priyabrata , Teng, Shyh , Zhang, Dengsheng , Liu, Ying , Lu, Guojun
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA); Sydney, Australia; 29th November-1st December 2017 p. 195-202
- Full Text: false
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- Description: Kernel descriptors have been proven to outperform existing histogram based local descriptors as such descriptors are extracted from the match kernels which measure similarities between image patches using different pixel attributes (gradient, colour or LBP pattern). The extraction of kernel descriptors does not require coarse quantization of pixel attributes. Instead, each pixel equally participates in matching between two image patches. In this paper, by leveraging the kernel properties, we propose a unique approach which simultaneously increases the effectiveness and efficiency of the existing kernel descriptors. Specifically, this is done by improving the similarity measure between two different patches in terms of any pixel attribute. The proposed kernel descriptors are more discriminant, take less time to be extracted and have much lower dimensions. Our experiments on Scene Categories and Caltech 101 databases show that our proposed approach outperforms the existing kernel descriptors.
Improved spatial pyramid matching for image classification
- Authors: Shahiduzzaman, Mohammad , Zhang, Dengsheng , Lu, Guojun
- Date: 2010
- Type: Text , Conference paper
- Relation: 10th Asian Conference on Computer Vision p. 449-459
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- Description: Spatial analysis of salient feature points has been shown to be promising in image analysis and classification. In the past, spatial pyramid matching makes use of both of salient feature points and spatial multiresolution blocks to match between images. However, it is shown that different images or blocks can still have similar features using spatial pyramid matching. The analysis and matching will be more accurate in scale space. In this paper, we propose to do spatial pyramid matching in scale space. Specifically, pyramid match histograms are computed in multiple scales to refine the kernel for support vector machine classification. We show that the combination of salient point features, scale space and spatial pyramid matching improves the original spatial pyramid matching significantly.
Improved Tamura features for image classification using kernel based descriptors
- Authors: Karmakar, Priyabrata , Teng, Shyh , Zhang, Dengsheng , Liu, Ying , Lu, Guojun
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA); Sydney, Australia; 29th November-1st December 2017 p. 461-467
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- Description: Tamura features are based on human visual perception and have huge potential in image representation. Conventional Tamura features only work on homogeneous texture images and perform poor on generic images. Therefore, many researchers attempt to improve Tamura features and most of the improvements are based on histogram based representation. Kernel descriptors have been shown to outperform existing histogram based local features as such descriptors do not require coarse quantization of pixel attributes. Instead, in kernel descriptor framework, each pixel equally participates in matching between two image patches. In this paper, we propose a set of kernel descriptors that are based on Tamura features. Additionally, the proposed descriptors are invariant to local rotations. Experimental results show that our proposed approach outperforms the conventional Tamura features significantly.
Integrating object ontology and region semantic template for crime scene investigation image retrieval
- Authors: Liu, Ying , Huang, Yuan , Zhang, Shuai , Zhang, Dengsheng , Ling, Nam
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA); Siem Reap, Cambodia; 18th-20th June 2017 p. 149-153
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- Description: Crime Scene Investigation (CSI) image retrieval plays an important role in solving crimes by providing useful clues for the police force. However, there has been little work done in this area due to limited public data access by researchers. Tested on real-world CSI images, it was observed that existing content-based image retrieval (CBIR) methods do not necessarily retrieve as effectively on CSI image database as compared to other general image databases. Hence, it is important to design CBIR algorithm tuned to CSI image database. This paper proposes a region-based semantic learning method based on object ontology which associates image categories with 'objects' in CSI images. Each object corresponds to a pre-defined semantic template (ST) which is defined as the average color and texture feature of a set of sample regions. In this way, low-level features of each region in a CSI image can be converted to an 'object' by comparing the region features with the set of pre-defined STs. The 'objects' in an image categorize the image based on the object ontology. The above process is referred to as 'On-Set'. To further improve retrieval performance of On-Set, a weighting strategy named object-frequency-based weighting (OFW) is designed inspired by the idea of term frequency-inverse document frequency (TF-IDF). In OFW, heavier weight is assigned to regions that appear more often in one class and less often in other classes. Experimental results on real-world image data proved the effectiveness of the proposed method for CSI image database retrieval.
Learning naive Bayes classifiers for music classification and retrieval
- 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
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- 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.
Learning sparse kernel classifiers for multi-instance classification
- Authors: Fu, Zhouyu , Lu, Guojun , Ting, Kaiming , Zhang, Dengsheng
- Date: 2013
- Type: Text , Journal article
- Relation: IEEE Transactions on Neural Networks and Learning Systems Vol. 24, no. 9 (2013), p. 1377-1389
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- Description: We propose a direct approach to learning sparse kernel classifiers for multi-instance (MI) classification to improve efficiency while maintaining predictive accuracy. The proposed method builds on a convex formulation for MI classification by considering the average score of individual instances for bag-level prediction. In contrast, existing formulations used the maximum score of individual instances in each bag, which leads to nonconvex optimization problems. Based on the convex MI framework, we formulate a sparse kernel learning algorithm by imposing additional constraints on the objective function to enforce the maximum number of expansions allowed in the prediction function. The formulated sparse learning problem for the MI classification is convex with respect to the classifier weights. Therefore, we can employ an effective optimization strategy to solve the optimization problem that involves the joint learning of both the classifier and the expansion vectors. In addition, the proposed formulation can explicitly control the complexity of the prediction model while still maintaining competitive predictive performance. Experimental results on benchmark data sets demonstrate that our proposed approach is effective in building very sparse kernel classifiers while achieving comparable performance to the state-of-the-art MI classifiers.
Learning sparse kernel classifiers in the primal
- Authors: Fu, Zhouyu , Lu, Guojun , Ting, Kaiming , Zhang, Dengsheng
- Date: 2012
- Type: Text , Conference paper
- Relation: Joint IAPR International Workshop, SSPR&SPR 2012; Hiroshima, Japan; 7th-9th November 2012; published in Structural, Syntactic, and Statistical Pattern Recognition (part of the Lecture Notes in Computer Science) Vol. 7626, p. 60-69
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- Description: The increasing number of classification applications in large data sets demands that efficient classifiers be designed not only in training but also for prediction. In this paper, we address the problem of learning kernel classifiers with reduced complexity and improved efficiency for prediction in comparison to those trained by standard methods. A single optimisation problem is formulated for classifier learning which optimises both classifier weights and eXpansion Vectors (XVs) that define the classification function in a joint fashion. Unlike the existing approach of Wu et al, which performs optimisation in the dual formulation, our approach solves the primal problem directly. The primal problem is much more efficient to solve, as it can be converted to the training of a linear classifier in each iteration, which scales linearly to the size of the data set and the number of expansions. This makes our primal approach highly desirable for large-scale applications, where the dual approach is inadequate and prohibitively slow due to the solution of cubic-time kernel SVM involved in each iteration. Experimental results have demonstrated the efficiency and effectiveness of the proposed primal approach for learning sparse kernel classifiers that clearly outperform the alternatives.