子区域视觉短语稀疏编码的图像检索 -- 西北工业大学学报,2015,33(5):721-726
论文:2015,Vol:33,Issue(5):721-726
引用本文:
王瑞霞, 彭国华. 子区域视觉短语稀疏编码的图像检索[J]. 西北工业大学学报
Wang Ruixia, Peng Guohua. Image Retrieval of Sub-Region Visual Phrases with Sparse Coding[J]. Northwestern polytechnical university

子区域视觉短语稀疏编码的图像检索
王瑞霞, 彭国华
西北工业大学 理学院, 陕西 西安 710129
摘要:
针对BOVW模型忽略图像特征空间排列导致量化误差较大的缺点,利用角点和特征点对图像进行区域分割,结合区域的空间排列信息,提出一种多通道融合的图像检索方法。其主要思想是将子区域编码和特征空间排列直方图结合组建视觉短语,这种构造方式在减少编码误差的同时还能更好地保留局部空间信息。首先,利用稀疏编码保留局部信息的高效性对提取的子区域进行编码;其次,利用特征的空间位置关系,计算子区域内的特征空间排列直方图;利用区域编码和特征排列直方图构建视觉短语;最后,结合BOVW模型的鲁棒性,统计视觉短语直方图用于图像检索。实验结果表明,该检索方法不仅比BOVW和SPMBOVM有更好的检索准确率,而且其编码过程稳定,误差较小。
关键词:    角点    BOVW模型    视觉短语    稀疏编码    图像检索    SPM模型   
Image Retrieval of Sub-Region Visual Phrases with Sparse Coding
Wang Ruixia, Peng Guohua
Department of Applied Mathemetics, Northwestern Polytechnical University, Xi'an, 710129, China
Abstract:
The BOVW model ignores the image feature spatial arrangement, thus causing quantization error. Considering this shortcoming, we divided an image into a series of sub-regions according to corners and features. Combining spatial arrangement information of the sub-regions, we, using multimodal fusion, proposed a new image retrieval method. The main idea is to construct visual phrases through sub-region encoding and feature spatial arrangement histograms. By this combination, it not only reduces the encoding error but also better preserves the local spatial information. First, using the advantages of sparse coding, we encoded the sub-regions; second, according to the feature spatial location relations, sub-region feature spatial arrangement histograms were calculated; third, visual phrases were composed of sub-region encoding and feature spatial arrangement histograms; at last, incorporating the robustness of BOVW model, we calculated the visual phrase histograms for image retrieval. The results and their analysis show preliminarily that the proposed retrieval method not only has better retrieval accuracy than BOVW and SPMBOVW but also its encoding is more stable and the error is smaller.
Key words:    calculations    clustering algorithms    combinatorial optimization    data fusion    errors    flowcharting    functions    image coding    image retrieval    image segmentation    mathematical operators    MATLAB    mean square error    pixels    robust control    schematic diagrams    stability    corner    BOVW(bag-of-visual-words) model    visual phrase    sparse code    spatial pyramid match model   
收稿日期: 2015-01-18     修回日期:
DOI:
基金项目: 国家自然科学基金(61201323)资助
通讯作者:     Email:
作者简介: 王瑞霞(1984—),女,西北工业大学博士研究生,主要从事基于内容的图像检索研究。
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