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基于多视觉码本的图像表示
引用本文:宋彦,蒋兵,戴礼荣.基于多视觉码本的图像表示[J].模式识别与人工智能,2013,26(10):909-915.
作者姓名:宋彦  蒋兵  戴礼荣
作者单位:中国科学技术大学 电子工程与信息科学系 科大讯飞语音实验室 合肥 230027
基金项目:国家自然科学基金资助项目
摘    要:基于词袋模型的图像表示方法的有效性主要受限于局部特征的量化误差。文中提出一种基于多视觉码本的图像表示方法,通过综合考虑码本构建和编码方法这两个方面的因素加以改进。具体包括:1)多视觉码本构建,以迭代方式构建多个紧凑且具有互补性的视觉码本;2)图像表示,首先针对多码本的情况,依次从各码本中选择相应的视觉单词并采用线性回归估计编码系数,然后结合图像的空间金字塔结构形成最终的图像表示。在一些标准测试集合的图像分类结果验证文中方法的有效性。

关 键 词:图像分类  视觉码本  聚类分析  图像表示  
收稿时间:2012-08-20

Image Representation Based on Multiple Visual Codebooks
SONG Yan , JIANG Bing , DAI Li-Rong.Image Representation Based on Multiple Visual Codebooks[J].Pattern Recognition and Artificial Intelligence,2013,26(10):909-915.
Authors:SONG Yan  JIANG Bing  DAI Li-Rong
Affiliation:iFlytek Speech Laboratory, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027
Abstract:The effectiveness of the image representation based on bag-of-visual words(BoW) model is majorly limited by the quantization error. To address this issue, an improved image representation based on multiple visual codebooks is proposed in this paper, which considers both visual codebook construction and feature coding. The proposed method specifically consists of 1) multiple visual codebooks construction, in which the compact and complementary visual codebooks are iteratively generated; 2) image representation, in which the visual words are firstly selected from each individual visual codebook, then the coding coefficients are determined by using the regularized linear regression method, and finally the image is represented by combining the spatial pyramid structure. The experimental results on several benchmark image classification datasets demonstrate the consistent and significant improvement of the proposed method.
Keywords:Image Classification  Visual Codebook  Clustering Analysis  Image Representation
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