实现SVM图像标注的偏色去除 [英] Implementation of SVM image annotation for color cast removal

问题描述

我实现一个自动偏色去除的基础上[的 1 ]，这似乎是一个功能强大，简单，但良好的执行方法。为避免除去的特性铸造由predominant颜色如植被的大区域，或水，它们使用由[ 2 。

色偏检测器使用多类支持向量机的图像区域划为天空，皮肤，植被，水或其他。我的问题是[ 2 ]只介绍方法，它们不包括从训练的SVM所得的超平面的参数。培训一个新的SVM是出路我的范围，但我还没有发现任何类似的作品，包括随时可以使用的数据。我真的AP preciate执行下列操作之一：

一个。一组使用的方法从训练产生的超平面参数[ 2 ]

乙。其他一些图像标注方法的天空/护肤/植物/水，其中包括训练有素的参数或不需要培训。

℃。含有的天空/护肤/植物/水注明的地区，我可以使用使用方法[ 2 。

参考

1. F。加斯帕里尼和R. SCHETTINI色彩平衡
2. ℃。 Cusano，G CIOCCA和R. SCHETTINI使用SVM的图像标注

解决方案

我从作者之一的答案，他们没有留下任何code或数据。相反，他向我指出 colorconstancy.com ，其中包含链接到两个源$ C ​​$ c和不同的图像数据库。他还提到 web.mit.edu/torralba/www/ ，包含注释的图像数据库我将用于训练的算法。

I'm implementing an automatic color cast removal based on [1], which seem like a robust, simple and yet well-performing method. To avoid removing an intrinsic cast by a predominant color such as large regions of vegetation, or water, they use a method of image annotation described by [2].

The color cast detector use a multiclass support vector machine to classify image regions as sky, skin, vegetation, water or other. My problem is that [2] only describes the method, they do not include the parameters of the hyperplanes resulting from training the SVM. Training a new SVM is way out of my scope, but I haven't found any similar works including ready-to-use data. I would really appreciate one of the following:

A. A set of hyperplane parameters resulting from training using the method in [2].

B. Some other image annotation method for sky/skin/vegetation/water, including trained parameters or not requiring training.

C. Some free image database containing annotated regions of sky/skin/vegetation/water, that I can use to train a new SVM using the method in [2].

References

1. F. Gasparini and R. Schettini "Color Balancing of Digital Photos Using Simple Image Statistics"
2. C. Cusano, G. Ciocca and R. Schettini "Image annotation using SVM"

解决方案

I got an answer from one of the authors, they don't have any code or data left. Instead he pointed me to colorconstancy.com, which contains links to both source code and different image databases. He also mentioned web.mit.edu/torralba/www/, containing an annotated image database that I will use for training the algorithm.

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