两种基于多特征融合的图像检索方法具有非常好的性能.但是,这两种融合方法存在以下问题:1) 在颜色空间中直接定义纹理结构的方法会增大对颜色特征的描述;2) 提取多种特征再重新融合为一个向量的方法,这种方法将有效的特征和无效的特征直接结合后,无效的特征会降低检索性能.针对以上问题,提出一种新的混合框架用于彩色图像检索,该框架使用词袋模型(bag-of-visual words, BoW)和颜色强度局部差分模式(color intensity-based local difference patterns, CILDP)分别提取图像的不同特征信息.同时,提出的融合框架利用graph density的方法将BoW和CILDP的排序结果进行有效融合,利用该框架能够提高图像检索的精度.在Corel-1K数据库上,返回10幅图像时,提出的框架的平均精度为86.26%,分别比CILDP和BoW提高了大约6.68%和12.53%.在不同数据库上的大量实验也验证了该框架在图像检索上的有效性.%Two lines of image representation based on multiple features fusion demonstrate excellent performance in image retrieval. However, there are some problems in both of them: 1) the methods defining directly texture in color space put more emphasis on color than texture feature; 2) the methods extract several features respectively and combine them into a vector, in which bad features may lead to worse performance after combining directly good and bad features. To address the problems above, a novel hybrid framework for color image retrieval through combination of local and global features achieves higher retrieval precision. The bag-of-visual words (BoW) models and color intensity-based local difference patterns (CILDP) are exploited to capture local and global features of an image. The proposed fusion framework combines the ranking results of BoW and CILDP through graph-based density method. The performance of our proposed framework in terms of average precision on Corel-1K database is 86.26%, and it improves the average precision by approximately 6.68% and 12.53% over CILDP and BoW, respectively. Extensive experiments on different databases demonstrate the effectiveness of the proposed framework for image retrieval.
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机译：两种基于多特征融合的图像检索方法具有非常好的性能.但是,这两种融合方法存在以下问题:1) 在颜色空间中直接定义纹理结构的方法会增大对颜色特征的描述;2) 提取多种特征再重新融合为一个向量的方法,这种方法将有效的特征和无效的特征直接结合后,无效的特征会降低检索性能.针对以上问题,提出一种新的混合框架用于彩色图像检索,该框架使用词袋模型(bag-of-visual words, BoW)和颜色强度局部差分模式(color intensity-based local difference patterns, CILDP)分别提取图像的不同特征信息.同时,提出的融合框架利用graph density的方法将BoW和CILDP的排序结果进行有效融合,利用该框架能够提高图像检索的精度.在Corel-1K数据库上,返回10幅图像时,提出的框架的平均精度为86.26%,分别比CILDP和BoW提高了大约6.68%和12.53%.在不同数据库上的大量实验也验证了该框架在图像检索上的有效性.%Two lines of image representation based on multiple features fusion demonstrate excellent performance in image retrieval. However, there are some problems in both of them: 1) the methods defining directly texture in color space put more emphasis on color than texture feature; 2) the methods extract several features respectively and combine them into a vector, in which bad features may lead to worse performance after combining directly good and bad features. To address the problems above, a novel hybrid framework for color image retrieval through combination of local and global features achieves higher retrieval precision. The bag-of-visual words (BoW) models and color intensity-based local difference patterns (CILDP) are exploited to capture local and global features of an image. The proposed fusion framework combines the ranking results of BoW and CILDP through graph-based density method. The performance of our proposed framework in terms of average precision on Corel-1K database is 86.26%, and it improves the average precision by approximately 6.68% and 12.53% over CILDP and BoW, respectively. Extensive experiments on different databases demonstrate the effectiveness of the proposed framework for image retrieval.
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