Image-recognition

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Image-recognition

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Image recognition methods from bag of words (BoW), Spatial Pyramid Matching (SPM), Sparse Codeing SPM (ScSPM) to convolutional neural networks (CNN)

Image-recognition 최신버젼 다운로드

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# Image-recognition
Image recognition methods from bag of words (BoW), Spatial Pyramid Matching (SPM), Sparse Codeing SPM (ScSPM) to convolutional neural networks (CNN) and CNN-SVM.

**Author:** *CyrusChiu* @ntu

## Included methods
-   Bag of words [1]
-   Spatial Pyramid Matching [2]
-   Sparse Coding SPM [3]
-   Convolutional neural networks [4]
-   CNN-SVM [5]

## Requirements
#### Basic (BoW, SPM, ScSPM)
-   Python 2.7
-   NumPy
-   SciPy
-   Scikit-learn
-   OpenCV 3.0.0 + opencv_contrib [installation instructions](http://www.pyimagesearch.com/2015/06/22/install-opencv-3-0-and-python-2-7-on-ubuntu/)

We use OpenCV here to load the image and extract SIFT descriptor only, you can use any image library if you want.
#### Optional (CNN, CNN-SVM)
-   keras
-   Caffe, pycaffe [installation instructions](http://caffe.berkeleyvision.org/installation.html)

## Demo
`example.py` training a SVM with SPM method on Caltech101 Dataset [6]

```
$python example.py --train Caltech101/DatasetFile.txt
```

## Usage
An end-to-end script with training and testing is provided

- Dataset  
**Dataset File** is a text file with the format of  **label** and **path** like:

```
  #train.txt
  label path/to/train1.jpg  
  label path/to/train2.jpg  
  label path/to/train3.jpg  
  ```
  ```
  #test.txt
  label path/to/test1.jpg
  label path/to/test2.jpg
  ```

- Training and evaluation on training set, predict on test set which is labeled 
  
  ```
  $python yourMethod.py --train path/to/train.txt --test path/to/test.py
  ```
  
  
  
### References
[1] CSURKA, Gabriella, et al. Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision, ECCV. 2004. p. 1-2.

[2] LAZEBNIK, Svetlana; SCHMID, Cordelia; PONCE, Jean. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. IEEE, 2006. p. 2169-2178.

[3] Jianchao Yang, Kai Yu, Yihong Gong, and Thomas Huang. Linear spatial pyramid matching using sparse coding for image classification. CVPR2009

[4] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.

[5] Girshick, Ross, et al. "Rich feature hierarchies for accurate object detection and semantic segmentation." Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on. IEEE, 2014.
2014.

[6] http://www.vision.caltech.edu/Image_Datasets/Caltech101/

[7] http://www.cs.toronto.edu/~kriz/cifar.html

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