This post is about image segmentation, as someone asked. My knowledge is poor in field of segmentation, but I have used a simple way of extracting feature, which is similar to a segmentation method. It uses image labeling to find the objects. In this method, color homogeneity of a region will be the criterion to define the objects.
First I explain the philosophy behind this method, then show you the implementation and its results.
You have to reduce the number of colors in you color pallet (result of quantizing color space) into some limited color batchs which we call them color labels, like: Blue, Dark Blue, Light Blue, Yellow, Light Yellow, etc. At this time you may have n Color labels. After that, you have to label each pixels of image with these labels. By doing this, these pixels will be divided into n classes. Then you can precess the relation of these pixels and find out the object of the images.
These are steps to do implement mentioned method:
Step 1: Create your own pallet .I reduced RGB color space into 216 colors of safe colors cube.
Step 2: Cluster this pallet into some labels. I Clusters these 216 colors into 6 clusters/classes/labels, and then each clusters into 6 clusters/classes/labels, so I can label my pixels by 6 or 36. clusters/classes/labels are shown in the figure below. Each bin of 36 bins are one class of 36 classes, and each batch of 6 batches are one of 6 classes.
Step3: Assign a unique value to each class, and then label each pixel of your images by these values. I applied both [1, .. , 6] and [1, .. , 36] labels on some images which is shown below:
Step 4: Each object will be defined by a homogeneous color layout. Define its region and use it!
Some complicated and high-precision methods are available. You can find it by using your keywords in google and …