spatial + saliency

Spatial analysis of salient feature points has been shown to be promising in image analysis and classification.

1. Bag-of-features (BoF)

  1. BOF treats an image as a collection of unordered appearance descriptors extracted from local patches;
  2. Then the patches or descriptors are quantized into discrete visual words of a codebook dictionary, and
  3. then the image histograms are compared and classified according to the dictionary

BUT it discards the spatial order of local descriptors

2. Spatial Pyramid Matching (in scale space): extension of the BoF model

To match salient feature pointsspatial multiresolution blocks in multiple scales to refine the kernel for SVM classification.

BUT, different blocks may have similar feature

people can recognize natural scenes while overlooking most of the details in it (i.e. the constituent objects).

Scale is an important aspect of local feature finding in prominent cue detection in images. E.g. SIFT, which uses the maxima/minima of neighboring scale space to find the interest points or key points of an image.

3. Scale-space theory

It present images as one parameter family of smoothed image, according to the size of the smoothing kernel used for suppressing fine-scale structures

4. Subdivide and Disorder principle

The essence of this principle is to partition the image into smaller blocks and calculate orderless statistics of low level image features (e.g., pixel value, gradient orientation, filter bank output etc.).

The subdivision method:

  • regular grid,
  • quad trees, and
  • flexible image windows

Improved Spatial Pyramid Matching for Image Classification ACCV10

Discriminative Compact Pyramids for Object and Scene Recognition ESV12

Spatial Pyramid Matching 

Spatial info in multiresolution histogram cvpr01

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