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
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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 hybrid data dependent dissimilarity measure for image retrieval
- Authors: Shojanazeri, Hamid , Teng, Shyh , Zhang, Dengsheng , 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. 141-148
- Full Text: false
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- Description: In image retrieval, an effective dissimilarity (or similarity) measure is required to retrieve the perceptually similar images. Minkowski-type distance is widely used for image retrieval, however it has its limitation. It focuses on distance between image features and ignores the data distribution of the image features, which can play an important role in measuring perceptual similarity of images. To address this limitation, a data dependent measure named m-p, which calculates the dissimilarity using the data distribution rather than geometric distance has been proposed recently. It considers two instances in a sparse region to be more similar than in a dense region. Relying only on data distribution and completely ignoring the geometric distance raise other limitations. This may result in finding two perceptually dissimilar instances similar due to being located in a sparse region or vice versa. We proposed a new hybrid dissimilarity measure and experimental results show that it addresses these limitations.
A kernel-based approach for content-based image retrieval
- Authors: Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun , Zhang, Dengsheng
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 International Conference on Image and Vision Computing New Zealand; Auckland, New Zealand; 19th-21st November 2018 p. 1-6
- Full Text: false
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- Description: Content-based image retrieval (CBIR) is a popular approach to retrieve images based on a query. In CBIR, retrieval is executed based on the properties of image contents (e.g. gradient, shape, color, texture) which are generally encoded into image descriptors. Among the various image descriptors, histogram-based descriptors are very popular. However, they suffer from the limitation of coarse quantization. In contrast, the use of kernel descriptors (KDES) is proven to be more effective than histogram-based descriptors in other applications, e.g. image classification. This is because, in the KDES framework, instead of the quantization of pixel attributes, each pixel equally takes part in the similarity measurement between two images. In this paper, we propose an approach for how the conventional KDES and its improved version can be used for CBIR. In addition, we have provided a detailed insight into the effectiveness of improved kernel descriptors. Finally, our experiment results will show that kernel descriptors are significantly more effective than histogram-based descriptors in CBIR.
A new image dissimilarity measure incorporating human perception
- Authors: Shojanazeri, Hamid , Teng, Shyh , Aryal, Sunil , Zhang, Dengsheng , Lu, Guojun
- Date: 2018
- Type: Text , Unpublished work
- Full Text:
- Description: Pairwise (dis) similarity measure of data objects is central to many applications of image anlaytics, such as image retrieval and classification. Geometric distance, particularly Euclidean distance ((
A novel perceptual dissimilarity measure for image retrieval
- Authors: Shojanazeri, Hamid , Zhang, Dengsheng , Teng, Shyh , Aryal, Sunil , 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 Vol. 2018-November, p. 1-6
- Full Text: false
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- Description: Similarity measure is an important research topic in image classification and retrieval. Given a type of image features, a good similarity measure should be able to retrieve similar images from the database while discard irrelevant images from the retrieval. Similarity measures in literature are typically distance based which measure the spatial distance between two feature vectors in high dimensional feature space. However, this type of similarity measures do not have any perceptual meaning and ignore the neighborhood influence in the similarity decision making process. In this paper, we propose a novel dissimilarity measure, which can measure both the distance and perceptual similarity of two image features in feature space. Results show the proposed similarity measure has a significant improvement over the traditional distance based similarity measure commonly used in literature.
- Description: International Conference Image and Vision Computing New Zealand
A review on automatic image annotation techniques
- 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
An annotation rule extraction algorithm for image retrieval
- Authors: Chen, Zeng , Hou, Jin , Zhang, Dengsheng , Qin, Xue
- Date: 2012
- Type: Text , Journal article
- Relation: Pattern Recognition Letters Vol. 33, no. 10 (2012), p.1257-1268
- Full Text: false
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- Description: Automatic image annotation can be used to facilitate semantic search in large image databases. However, retrieval performance of the existing annotation schemes is far from the users’ expectation. In this paper, we propose a novel method to automatically annotate image through the rules generated by support vector machines and decision trees. In order to obtain the rules, we collect a set of training regions by image segmentation, feature extraction and discretization. We first employ a support vector machine as a preprocessing technique to refine the input training data and then use it to improve the rules generated by decision tree learning. The preprocessing can effectively deal with the similar regions in an image as well. Moreover, we integrate the original rules to the modified ones, so as to formulate the complete and effective annotation rules. We can translate an unknown image into text by this algorithm, and the proposed system can retrieve images queried by both images and keywords. Experiments are carried out in a standard Corel dataset and images collected from the Web to test the accuracy and robustness of the proposed system. Experimental results show the proposed algorithm can annotate and retrieve images more efficiently than traditional learning algorithms.
An enhancement to the spatial pyramid matching for image classification and retrieval
- Authors: Karmakar, Priyabrata , Teng, Shyh , Lu, Guojun , Zhang, Dengsheng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 22463-22472
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- Description: Spatial pyramid matching (SPM) is one of the widely used methods to incorporate spatial information into the image representation. Despite its effectiveness, the traditional SPM is not rotation invariant. A rotation invariant SPM has been proposed in the literature but it has many limitations regarding the effectiveness. In this paper, we investigate how to make SPM robust to rotation by addressing those limitations. In an SPM framework, an image is divided into an increasing number of partitions at different pyramid levels. In this paper, our main focus is on how to partition images in such a way that the resulting structure can deal with image-level rotations. To do that, we investigate three concentric ring partitioning schemes. Apart from image partitioning, another important component of the SPM framework is a weight function. To apportion the contribution of each pyramid level to the final matching between two images, the weight function is needed. In this paper, we propose a new weight function which is suitable for the rotation-invariant SPM structure. Experiments based on image classification and retrieval are performed on five image databases. The detailed result analysis shows that we are successful in enhancing the effectiveness of SPM for image classification and retrieval. © 2013 IEEE.
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.
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
- Full Text: false
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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
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
- Full Text: false
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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
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 region based image retrieval
- 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
- Full Text: false
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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.