Boosting of factorial correspondence analysis for image retrieval
Boosting of factorial correspondence analysis for image retrieval
复制标题
促进图像检索的阶乘对应分析
DOI：
10.1109/cbmi.2008.4564950
复制
发表时间：
2008
期刊：
影响因子：
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通讯作者：
P. Gros
中科院分区：
文献类型：
--
作者：
Nguyen;A. Morin;P. Gros
我们对使用因子对应分析 (FCA) 进行图像检索感到担忧。 FCA 旨在分析列联表。在文本数据分析 (TDA) 中，FCA 分析交叉术语/单词和文档的列联表。为了在图像上采用 FCA，我们首先定义根据图像中的可扩展不变特征变换 (SIFT) 描述符计算的“视觉词”，并将它们用于图像量化。在这一步，我们可以构建一个列联表，将“视觉词”作为术语/单词，将图像作为文档。尽管FCA在信息检索中取得了成功的应用，但由于大矩阵的对角化，FCA仍然面临着大维数问题。我们提出了一种新算法 CABoost，它克服了 FCA 的大维问题。数据按列（字）采样，并对样本应用 FCA。经过一些采样后，我们最终通过加权主成分分析（PCA）将分离的结果组合起来。数值实验表明，我们的算法比经典 FCA 执行得更快，而且不会损失精度。
We are concerned by the use of factorial correspondence analysis (FCA) for image retrieval. FCA is designed for analysing contingency tables. In textual data analysis (TDA), FCA analyses a contingency table crossing terms/words and documents. For adapting FCA on images, we first define rdquovisual wordsrdquo computed from scalable invariant feature transform (SIFT) descriptors in images and use them for image quantization. At this step, we can build a contingency table crossing rdquovisual wordsrdquo as terms/words and images as documents. In spite of its successful applications in information retrieval, FCA suffers from large dimension problem because of the diagonalization of a large matrix. We propose a new algorithm, CABoost, which overcomes this large dimension problem of FCA. The data are sampled by column (word) and a FCA is applied on the sample. After some samplings, we finally combine separated results by a weighting - principle component analysis (PCA). The numerical experiments show that our algorithm performs more rapidly than the classical FCA without losing precision.
以1类整合子为指标的城市河流耐药基因污染评价
DOI：
--
发表时间：
2021
期刊：
影响因子：
--
作者：
L. Lihui;X. Zou;W. Dai;D. Xue;T. Nakamura;A. Wakamiya;K. Marumoto;増田容一，石川将人;長澤杏香，春日郁朗，栗栖太，古米弘明
通讯作者：
長澤杏香，春日郁朗，栗栖太，古米弘明