Stanford cs231n Assigenment #1 (e) 基于Feature分类

1.Histogram of Gradients

具体HoG的原理参见本博客计算机视觉的一篇转载文章，以下是课程提供的代码：

def hog_feature(im):
  """Compute Histogram of Gradient (HOG) feature for an image
  
       Modified from skimage.feature.hog
       http://pydoc.net/Python/scikits-image/0.4.2/skimage.feature.hog
     
     Reference:
       Histograms of Oriented Gradients for Human Detection
       Navneet Dalal and Bill Triggs, CVPR 2005
     
    Parameters:
      im : an input grayscale or rgb image
      
    Returns:
      feat: Histogram of Gradient (HOG) feature
    
  """
  
  # convert rgb to grayscale if needed
  if im.ndim == 3:
    image = rgb2gray(im)
  else:
    image = np.at_least_2d(im)

  sx, sy = image.shape # image size
  orientations = 9 # number of gradient bins
  cx, cy = (8, 8) # pixels per cell

  gx = np.zeros(image.shape)
  gy = np.zeros(image.shape)
  gx[:, :-1] = np.diff(image, n=1, axis=1) # compute gradient on x-direction
  gy[:-1, :] = np.diff(image, n=1, axis=0) # compute gradient on y-direction
  grad_mag = np.sqrt(gx ** 2 + gy ** 2) # gradient magnitude
  grad_ori = np.arctan2(gy, (gx + 1e-15)) * (180 / np.pi) + 90 # gradient orientation

  n_cellsx = int(np.floor(sx / cx))  # number of cells in x
  n_cellsy = int(np.floor(sy / cy))  # number of cells in y
  # compute orientations integral images
  orientation_histogram = np.zeros((n_cellsx, n_cellsy, orientations))
  for i in range(orientations):
    # create new integral image for this orientation
    # isolate orientations in this range
    temp_ori = np.where(grad_ori < 180 / orientations * (i + 1),
                        grad_ori, 0)
    temp_ori = np.where(grad_ori >= 180 / orientations * i,
                        temp_ori, 0)
    # select magnitudes for those orientations
    cond2 = temp_ori > 0
    temp_mag = np.where(cond2, grad_mag, 0)
    orientation_histogram[:,:,i] = uniform_filter(temp_mag, size=(cx, cy))[cx/2::cx, cy/2::cy].T
  
  return orientation_histogram.ravel()

2.Color Histogram

代码如下：

def color_histogram_hsv(im, nbin=10, xmin=0, xmax=255, normalized=True):
  """
  Compute color histogram for an image using hue.

  Inputs:
  - im: H x W x C array of pixel data for an RGB image.
  - nbin: Number of histogram bins. (default: 10)
  - xmin: Minimum pixel value (default: 0)
  - xmax: Maximum pixel value (default: 255)
  - normalized: Whether to normalize the histogram (default: True)

  Returns:
    1D vector of length nbin giving the color histogram over the hue of the
    input image.
  """
  ndim = im.ndim
  bins = np.linspace(xmin, xmax, nbin+1)
  hsv = matplotlib.colors.rgb_to_hsv(im/xmax) * xmax
  imhist, bin_edges = np.histogram(hsv[:,:,0], bins=bins, density=normalized)
  imhist = imhist * np.diff(bin_edges)

  # return histogram
  return imhist

3. Extract Features

将两种feature存在同一个col中，line的index是第n张图。用两种feature的意义在于，color histogram可以反应颜色特征，而HoG可以反应texture特征。代码如下：

def extract_features(imgs, feature_fns, verbose=False):
  """
  Given pixel data for images and several feature functions that can operate on
  single images, apply all feature functions to all images, concatenating the
  feature vectors for each image and storing the features for all images in
  a single matrix.

  Inputs:
  - imgs: N x H X W X C array of pixel data for N images.
  - feature_fns: List of k feature functions. The ith feature function should
    take as input an H x W x D array and return a (one-dimensional) array of
    length F_i.
  - verbose: Boolean; if true, print progress.

  Returns:
  An array of shape (N, F_1 + ... + F_k) where each column is the concatenation
  of all features for a single image.
  """
  num_images = imgs.shape[0]
  if num_images == 0:
    return np.array([])

  # Use the first image to determine feature dimensions
  feature_dims = []
  first_image_features = []
  for feature_fn in feature_fns:
    feats = feature_fn(imgs[0].squeeze())
    assert len(feats.shape) == 1, 'Feature functions must be one-dimensional'
    feature_dims.append(feats.size)
    first_image_features.append(feats)

  # Now that we know the dimensions of the features, we can allocate a single
  # big array to store all features as columns.
  total_feature_dim = sum(feature_dims)
  imgs_features = np.zeros((num_images, total_feature_dim))
  imgs_features[0] = np.hstack(first_image_features).T

  # Extract features for the rest of the images.
  for i in xrange(1, num_images):
    idx = 0
    for feature_fn, feature_dim in zip(feature_fns, feature_dims):
      next_idx = idx + feature_dim
      imgs_features[i, idx:next_idx] = feature_fn(imgs[i].squeeze())
      idx = next_idx
    if verbose and i % 1000 == 0:
      print 'Done extracting features for %d / %d images' % (i, num_images)

  return imgs_features

4.在features的基础上用SVM和two_layer_net分别train

结果：利用validation set选取最优的hyper parameter，SVM达到了42%左右，而two_layer_net达到了55%左右。