WND-CHARM: Multi-purpose image classification using compound image transforms
WND-CHARM: Multi-purpose image classification using compound image transforms
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DOI：
10.1016/j.patrec.2008.04.013
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发表时间：
2008-08-01
影响因子：
5.1
通讯作者：
Goldberg, Ilya G.
中科院分区：
文献类型：
--
作者：
Orlov, Nikita;Shamir, Lior;Goldberg, Ilya G.
我们描述了一种多用途图像分类器，无需修改或微调即可应用于各种图像分类任务，并且提供与最先进的特定任务图像分类器相当的分类精度。所提出的图像分类器首先提取大量 1025 个图像特征，包括多项式分解、高对比度特征、像素统计和纹理。这些特征是在原始图像、图像的变换以及图像的变换的变换上计算的。然后使用特征值将测试图像分类为一组预定义的图像类别。该分类器在几个不同的问题上进行了测试，包括生物图像分类和人脸识别。尽管我们不能声称具有普遍性，但我们的实验结果表明，该分类器的性能与专门为这些图像分类任务开发的分类器一样好甚至更好。我们的分类器在各种分类问题上的高性能归因于（i）从图像中提取的大量特征； (ii) 对特定图像分类问题敏感的有效特征选择和加权算法。这些算法可以从 http://www.openmicroscopy.org 免费下载。由 Elsevier B.V. 出版
We describe a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provide classification accuracy comparable to state-of-the-art task-specific image classifiers. The proposed image classifier first extracts a large set of 1025 image features including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are computed on the raw image, transforms of the image, and transforms of transforms of the image. The feature values are then used to classify test images into a set of pre-defined image classes. This classifier was tested on several different problems including biological image classification and face recognition. Although we cannot make a claim of universality, our experimental results show that this classifier performs as well or better than classifiers developed specifically for these image classification tasks. Our classifier's high performance on a variety of classification problems is attributed to (i) a large set of features extracted from images; and (ii) an effective feature selection and weighting algorithm sensitive to specific image classification problems. The algorithms are available for free download from http://www.openmicroscopy.org. Published by Elsevier B.V.