Separable vocabulary and feature fusion for image retrieval based on sparse representation
Separable vocabulary and feature fusion for image retrieval based on sparse representation
复制标题
基于稀疏表示的图像检索可分离词汇与特征融合
DOI：
10.1016/j.neucom.2016.08.106
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发表时间：
2017
期刊：
影响因子：
6
通讯作者：
Wang Hengyou
中科院分区：
文献类型：
--
作者：
Wang Yanhong;Cen Yigang;Zhao Ruizhen;Cen Yi;Hu Shaohai;Voronin Viacheslay;Wang Hengyou
视觉词汇是图像检索中视觉词袋（BOW）模型的核心。传统的检索方法为了保证检索的准确性，往往使用较大的词汇量。然而，大的词汇量会导致低召回。为了提高召回率，建议使用中等大小的词汇表，但这会导致准确率较低。为了解决这两个问题，我们提出了一种新的方法，基于特征融合和稀疏表示的可分离词汇的图像检索。首先，在训练数据集上生成大量词汇。其次，将词汇表分成若干个中等大小的词汇表。第三，对于给定的查询图像，我们采用稀疏表示来选择一个词汇进行检索。在所提出的方法中，大的词汇量可以保证相对较高的准确率，而与中等大小的词汇负责高召回。同时，为了减少量化误差和提高召回率，采用稀疏表示方法对视觉词进行量化。此外，还融合了局部特征和全局特征以提高召回率。我们提出的方法在两个基准数据集上进行了评估，即，第20章和假期实验表明，该方法具有良好的性能.
Visual vocabulary is the core of the Bag-of-visual-words (BOW) model in image retrieval. In order to ensure the retrieval accuracy, a large vocabulary is always used in traditional methods. However, a large vocabulary will lead to a low recall. In order to improve recall, vocabularies with medium sizes are proposed, but they will lead to a low accuracy. To address these two problems, we propose a new method for image retrieval based on feature fusion and sparse representation over separable vocabulary. Firstly, a large vocabulary is generated on the training dataset. Secondly, the vocabulary is separated into a number of vocabularies with medium sizes. Thirdly, for a given query image, we adopt sparse representation to select a vocabulary for retrieval. In the proposed method, the large vocabulary can guarantee a relatively high accuracy, while the vocabularies with medium sizes are responsible for high recall. Also, in order to reduce quantization error and improve recall, sparse representation scheme is used for visual words quantization. Moreover, both the local features and the global features are fused to improve the recall. Our proposed method is evaluated on two benchmark datasets, i.e., Coil20 and Holidays. Experiments show that our proposed method achieves good performance.