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
- Bag of words [1]
- Spatial Pyramid Matching [2]
- Sparse Coding SPM [3]
- Convolutional neural networks [4]
- CNN-SVM [5]
- Python 2.7
- NumPy
- SciPy
- Scikit-learn
- OpenCV 3.0.0 + opencv_contrib installation instructions
We use OpenCV here to load the image and extract SIFT descriptor only, you can use any image library if you want.
- keras
- Caffe, pycaffe installation instructions
example.py training a SVM with SPM method on Caltech101 Dataset [6]
$python example.py --train Caltech101/DatasetFile.txt
An end-to-end script with training and testing is provided
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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
[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/