Today’s Internet giants value machine learning so much, of course not for the academic value mainly because it can bring great commercial value. So why did traditional algorithms not achieve the precision of deep learning before?
Before the deep learning algorithm, for the visual algorithm, it can be roughly divided into five steps: Feature Perception, Image Preprocessing, Feature Extraction, Feature Selection, Inference Prediction and Recognition.
Among the early dominant statistical machine learning projects, they were less concerned with the feature parts. So computer vision has to design the first four parts when using these machine learning methods, which is a difficult task for anyone. The traditional computer image recognition method separates the feature extraction and the classifier design, then merges them together in the application. For example, if the input is a motorcycle image, there must first be a feature expression or feature extraction process, and then put the expressed features into the learning algorithm for classification learning.
There have been many excellent feature operators in the past 20 years, such as the most famous Scale Invariant Feature Transform (SIFT) operator, which is based on scale space and maintains invariance to image scaling, rotation, and even affine transformation. SIFT extracts the local features of the image, finds the extreme points in the scale space and extracts the position, scale and direction information. SIFT’s applications include object recognition, robot map perception and navigation, effect stitching, 3D model building, gesture recognition, and effect tracking.
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