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
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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.
Combining pyramid match kernel and spatial pyramid for image classification
- Authors: Karmakar, Priyabrata , Teng, Shyh , Zhang, Dengsheng , Lu, Guojun , Liu, Ying
- Date: 2016
- Type: Text
- Relation: 2016 International Conference on Digital Image Computing: Techniques and Applications (Dicta); Gold Coast, Australia; 30th November-2nd December 2016 p. 486-493
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- Description: This paper proposes a new approach for image classification by combining pyramid match kernel (PMK) with spatial pyramid. Unlike the conventional spatial pyramid matching (SPM) approach which only uses a single-resolution feature vector to represent an image, we use a multi-resolution feature vector to represent an image for SPM. We then calculate the match scores at each resolution of SPM representation and finally compute the matching between two images by applying the concept of PMK using the match scores obtained from the multiple resolutions. Our experimental results show that the proposed combined pyramid matching achieves a significant improvement on classification performance.
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
- Full Text: false
- Reviewed:
- 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 line weber local descriptor and deep feature for tire indentation mark image classification
- Authors: Liu, Ying , Che, Xin , Dong, Haitao , Li, Daxiang , Teng, Shyh , Lu, Guojun
- Date: 2021
- Type: Text , Conference paper
- Relation: 4th International Conference on Artificial Intelligence and Pattern Recognition, 4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021,Virtual, Online,17-19 September 2021, 2021, ACM International Conference Proceeding Series p. 56-61
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- Description: Tire indentation mark matching is an essential tool used for the investigation of criminal cases and traffic incidents. As such images are unique and uncommon, there is a lack of dedicated databases and relevant research on this topic. This paper presents a feature extraction algorithm effective for tire indentation mark image description. The main contributions include: (1) Line feature Weber local descriptor (LWLD) is proposed, which uses the Gabor orientations instead of the original gradient orientation. This feature can describe texture information of tire indentation mark image more efficiently. (2) An attention model is constructed to produce attention feature map of tire indentation mark image. This attention feature map is then fused with LWLD resulting in a feature with more powerful representation capability. Experimental results prove that the combined use of LWLD and attention model greatly enhances the performance of tire indentation mark image matching tasks. © 2021 ACM.