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论文:2013,Vol:31,Issue(2):206-209 |
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引用本文: |
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张艳邦, 韩军伟, 郭雷, 许明. 利用稀疏表达检测多幅图像协同显著性目标[J]. 西北工业大学 |
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Zhang Yanbang, Han Junwei, Guo Lei, Xu Ming. A New Algorithm for Detecting Co-Saliency in Multiple Images through Sparse Coding Representation[J]. Northwestern polytechnical university |
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利用稀疏表达检测多幅图像协同显著性目标 |
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张艳邦, 韩军伟, 郭雷, 许明 |
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西北工业大学 自动化学院, 陕西 西安 710072 |
摘要: |
提出了一种利用稀疏表达检测多幅图像中协同显著目标的方法。首先用独立变量分析方法训练得到自然图像一组稀疏基,接着求出检测图像的稀疏表达,然后定义了多变量K-L散度度量它们之间的相似性,最后,根据K-L散度性质找出散度下降明显的地方,检测出多幅图像的共同显著性目标。实验结果表明,该方法正确有效,具有和人类视觉特性相符合的显著性目标检测效果。 |
关键词:
算法
图像处理
独立变量分析
协同显著性
稀疏表达
K-L散度
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A New Algorithm for Detecting Co-Saliency in Multiple Images through Sparse Coding Representation |
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Zhang Yanbang, Han Junwei, Guo Lei, Xu Ming |
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Department of Automatic Control,Northwestern Polytechnical University,Xi'an 710072,China |
Abstract: |
We propose what we believe to be a new algorithm for detecting the co-saliency in multiple images.First,we use the independent component analysis to learn and obtain a set of sparse bases of a natural imagethrough filtering the input image and then use them to work out the sparse coding representation of the image to bedetected. Second,we define the multi-variable Kullback-Leibler (K-L) divergence to measure the similarity amongmultiple images. Third,according to the properties of the K-L divergence,we detect the region where the diver-gence decreases significantly,or the similarity of the image,thus detecting the co-saliency in multiple images. Toverify the effectiveness of our algorithm,we test the image co-saliency detection effect with the photos we took. Thetest results,given in Fig. 3,and their analysis show preliminarily that the image co-saliency detection effect of ournew algorithm is the same as that of human visual characteristics. |
Key words:
algorithm
image processing
independent component analysis;co-saliency
sparse coding representa-tion
Kullback-Leibler divergence
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收稿日期: 2012-05-15
修回日期:
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DOI: |
基金项目: 国家自然科学基金(61273362);西北工业大学基础研究基金(NPU-FFR-JC201041)资助 |
通讯作者:
Email: |
作者简介: 张艳邦(1980-),西北工业大学博士研究生,主要从事计算机视觉及模式识别等研究。
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相关功能 |
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作者相关文章 |
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张艳邦 在本刊中的所有文章 |
韩军伟 在本刊中的所有文章 |
郭雷 在本刊中的所有文章 |
许明 在本刊中的所有文章 |
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参考文献: |
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[1] Achanta R,Hemami S,Estrada F,Susstrunk S. Frequency-Tuned Salient Region Detection. Proceedings of IEEE Conferenceon Computer Vision and Pattern Recognition (CVPR), 2011, 1597-1604 [2] Cheng M,Zhang G,Mitra N J,et al. Global Contrast Based Salient Region Detection. Proceedings of IEEE Conference onComputer Vision and Pattern Recognition (CVPR), 2011 [3] Chen H. Preattentive Co-Saliency Detection. Proceedings of 17th International Conference on Image Processing,Hong Kong,2010, 1117-1120 [4] Li H,Ngan N. A Co-Saliency Model of Image Pairs. IEEE Trans on Image Processing, 2011, 20(12): 3365-3375 [5] David E,Dan B,Eli S. Cosaliency: Where People Look When Comparing Images. Proceedings of 23rd Annual ACM Symposi-um on User Interface Software and Technology, 2010, 219-227 [6] Wang M,Konrad J,Ishwar P,Jing K,Rowley H. Image Saliency: from Intrinsic to Extrinsic Context. Proceedings of Confer-ence on Computer Vision and Pattern Recognition, 2011, 417-424 [7] Olshausen A. Emergence of Simple-Cell Receptive Field Properties by Learning a Sparse Code for Natural Images. Nature, 1996 [8] Bell A,Sejnoiwski T. The Independent Components of Natural Scenes Are Edge Filters. Vision Research,37(23): 3327-3338, 1997 [9] Hou X,Zhang L. Dynamic Visual Attention: Searching for Coding Length Increments. Proceedings of the Neural InformationProcessing Systems, 2008, 681-688 |
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