图 书 馆
K-NN框架下对小波系数补丁Kullback散度的图像检索Image Retrieval via Kullback Divergence of Patches of Wavelets Coefficients in the k-NN Framework |
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| 课程网址: | http://videolectures.net/etvc08_barlaud_irvkd/ |
| 主讲教师: | Michel Barlaud |
| 开课单位: | 尼斯索菲亚安蒂波利斯大学 |
| 开课时间: | 2008-12-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This talk presents a framework to define an objective measure of the similarity (or dissimilarity) between two images for image processing. The problem is twofold: * define a set of features that capture the information contained in the image relevant for the given task and * define a similarity measure in this feature space. In this paper, we propose a feature space as well as a statistical measure on this space. Our feature space is based on a global description of the image in a multiscale transformed domain. After decomposition into a Laplacian pyramid, the coefficients are arranged in intrascale/ interscale/interchannel patches which reflect the dependencies of neighboring coefficients in presence of specific structures or textures. At each scale, the probability density function (pdf) of these patches is used as a description of the relevant information. Because of the sparsity of the multiscale transform, the most significant patches, called Sparse Multiscale Patches (SMP), describe efficiently these pdfs. We propose a statistical measure (the Kullback-Leibler divergence) based on the comparison of these probability density function. Interestingly, this measure is estimated via the nonparametric, k-th nearest neighbor framework without explicitly building the pdfs. This framework is applied to a query-by-example image retrieval method. Experiments on two publicly available databases showed the potential of our SMP approach for this task. In particular, it performed comparably to a SIFT-based retrieval method and two versions of a fuzzy segmentation-based method (the UFM and CLUE methods), and it exhibited some robustness to different geometric and radiometric deformations of the images. |
| 关 键 词: | 特征空间; 相似性度量; 概率密度函数; 稀疏变换 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-11-30：lxf |
| 阅读次数: | 52 |