change HOG cell size in trainCasca​deObjectDe​tector

2014 6 27
1 个回答
更新时间：2014 6 30
16 次查看（30 天）

1 个投票

I am using trainCascadeObjectDetector to train a detector for small objects so the default HOG features with 8x8 cell size is too big, is there any way to use 4x4 or 2x2 cell size for underlying HOG feature? thanks,

请先登录，再回答此问题。

回答（1 个）

Dima Lisin
Dima Lisin 2014-6-29
编辑：Dima Lisin 2014-6-29

0 个投票

Unfortunately, there is currently no way to do this. Is there any possibility for you to get higher resolution images? Or will there be enough detail if you up-sample the images?
Another possible alternative is to use the extractHOGFeatures function to compute the HOG features yourself. Then you could train a classifier (e. g. SVM) using the Statistics Toolbox. Take a look at this example of digit classification using HOG and SVM.
Another thought: a 2x2 cell seems really small for computing a 9-bin histogram. Training a HOG-SVM classifier for your objects might tell you whether it is feasible to detect them at all.

1 个评论
显示 -1更早的评论 隐藏 -1更早的评论

Alan
Alan 2014-6-30
Hi, Dima: thanks answering my question. I think your suggestion of using extractHOGFeatures to compute finer HOG feature with finer cells and train it with SVM classifier is good to try. I will let you know how it goes. As far as the granularity of cell size, my understanding is the finer the cell gets, the longer the feature vector will be and therefore, the dimension gets higher. The training of the classifier might be harder in general, therefore, the corresponding historgram bin number might also need to be tuned accordingly.

请先登录，再进行评论。

类别

在 帮助中心 和 File Exchange 中查找有关 Image Processing and Computer Vision 的更多信息

产品

标签

提问:

Alan
Alan
2014-6-27

评论：

Alan
Alan
2014-6-30

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by