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I've started working with computer vision techniques quite a bit, mainly deep learning but I want to try and get a good understanding of the more traditional techniques as well for a good grounding. I have been playing around with some manual feature engineering techniques for classification with RF and SVM classifiers. I've looked at texture representations like HOG and LBP descriptors as well as edge filters, gabor filters and spacial features such as fourier descriptors. What i'm kind of lacking is a good idea of how the different features group and what categories they each belong to. I know some are defined as global and local but what does this mean exactly and which ones? and are there others categories like texture and geometric that I should consider? Any explanation would be useful and much appreciated (i've looked a lot online but it all seems a bit fragmented)

Thanks!

maracuja
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Features are the information extracted from images in terms of numerical values that are difficult to understand and correlate by human. Suppose we consider the image as data the information extracted from the data is known as features. Generally, features extracted from an image are of much more lower dimension than the original image. The reduction in dimentionality reduces the overheads of processing the bunch of images.

Basically there are two types of features are extracted from the images based on the application. They are local and global features. Features are sometimes referred to as descriptors. Global descriptors are generally used in image retrieval, object detection and classification, while the local descriptors are used for object recognition/identification. There is a large difference between detection and identification. Detection is finding the existence of something/object (Finding whether an object is exist in image/video) where as Recognition is finding the identity (Recognizing a person/object) of an object.

Global features describe the image as a whole to the generalize the entire object where as the local features describe the image patches (key points in the image) of an object. Global features include contour representations, shape descriptors, and texture features and local features represents the texture in an image patch. Shape Matrices, Invariant Moments (Hu, Zerinke), Histogram Oriented Gradients (HOG) and Co-HOG are some examples of global descriptors. SIFT, SURF, LBP, BRISK, MSER and FREAK are some examples of local descriptors.

Generally, for low level applications such as object detection and classification, global features are used and for higher level applications such as object recognition, local features are used. Combination of global and local features improves the accuracy of the recognition with the side-effect of computational overheads.

Mehdi Hamzeloee
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