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中文摘要：
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。
英文摘要：
The BOVW model ignores the image feature spatial sidering this shortcoming, we divided an image into a series of bining spatial arrangement information of the sub-regions, we, trieval method. The main idea is to construct visual phrases arrangement, thus causing quantization error. Con- sub-regions according to corners and features. Com- using multimodal fusion, proposed a new image re- through sub-region encoding and feature spatial arrange- ment histograms. By this combination, it not only reduces the encoding error but also better preserves the local spa- tial 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, incorpora- ting 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.
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同项目期刊论文
Local discriminant non-negative matrix factorization feature extraction for hyperspectral image clas
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