Different feature types of image

Given an input image, a GISTdescriptor is computed by

• convolve the image with 32 Gabor filters at 4 scales, 8 orientations, producing 32 feature maps of the same size of the input image.
• divide each feature map into 16 regions (by a 4x4 grid), and then average the feature values within each region.
• Concatenate the 16 averaged values of all 32 feature maps, resulting in a 16x32=512 GIST descriptor. Intuitively, GIST summarizes the gradient information (scales and orientations) for different parts of an image, which provides a rough description (the gist) of the scene.

The LBP feature vector, in its simplest form, is created in the following manner:

• Divide the examined window into cells (e.g. 16x16 pixels for each cell).
• For each pixel in a cell, compare the pixel to each of its 8 neighbors (on its left-top, left-middle, left-bottom, right-top, etc.). Follow the pixels along a circle, i.e. clockwise or counter-clockwise.
• Where the center pixel's value is greater than the neighbor's value, write "0". Otherwise, write "1". This gives an 8-digit binary number (which is usually converted to decimal for convenience).
• Compute the histogram, over the cell, of the frequency of each "number" occurring (i.e., each combination of which
• pixels are smaller and which are greater than the center). This histogram can be seen as a 256-dimensional feature vector.
• Optionally normalize the histogram.
• Concatenate (normalized) histograms of all cells. This gives a feature vector for the entire window.
• The feature vector can now be processed using the Support vector machine or some other machine-learning algorithm to classify images. Such classifiers can be used for face recognition or texture analysis.

A useful extension to the original operator is the so-called uniform pattern[8], which can be used to reduce the length of the feature vector and implement a simple rotation invariant descriptor. This idea is motivated by the fact that some binary patterns occur more commonly in texture images than others. A local binary pattern is called uniform if the binary pattern contains at most two 0-1 or 1-0 transitions. For example, 00010000(2 transitions) is a uniform pattern, 01010100(6 transitions) is not. In the computation of the LBP histogram, the histogram has a separate bin for every uniform pattern, and all non-uniform patterns are assigned to a single bin. Using uniform patterns, the length of the feature vector for a single cell reduces from 256 to 59. The 58 uniform binary patterns correspond to the integers 0, 1, 2, 3, 4, 6, 7, 8, 12, 14, 15, 16, 24, 28, 30, 31, 32, 48, 56, 60, 62, 63, 64, 96, 112, 120, 124, 126, 127, 128, 129, 131, 135, 143, 159, 191, 192, 193, 195, 199, 207, 223, 224, 225, 227, 231, 239, 240, 241, 243, 247, 248, 249, 251, 252, 253, 254 and 255.

HSLand HSVare the two most common cylindrical-coordinate representations of points in an RGBcolor model. The two representations rearrange the geometry of RGBin an attempt to be more intuitive and perceptually relevant than the cartesian (cube) representation. Developed in the 1970s for computer graphics applications, HSLand HSVare used today in color pickers, in image editing software, and less commonly in image analysis and computer vision.

HSL stands for hue, saturation, and lightness (or luminosity), and is also often called HLS. HSVstands for hue, saturation, and value, and is also often called HSB(B for brightness). A third model, common in computer vision applications, is HSI(I for intensity). However, while typically consistent, these definitions are not standardized, and any of these abbreviations might be used for any of these three or several other related cylindrical models.