Computer vision, when a computer and/or machine have sight, can be used in many applications like OCR, Vision Biometrics, Object Recognition, Social Media, Smart Cars etc., Different approach evolved over a period of time in computer vision problems, which can be categorized as, one after the deep learning in computer vision problem and the other before deep learning in computer vision problem. The prior one named as classical approach (HOG & SIFT., etc), could not learn from discrimination features from images and non adoptive for diverse image and doesn’t meet human level of accuracy. So there arises a requirement for learning method in computer vision Problems. Machine learning gives computers the ability to learn without being explicitly programmed. Deep learning or machine learning overcomes the drawbacks of classical approach by learning the features in the images and the diversity in the images implicitly and thus meets more accuracy than human vision. In this paper we will study difference methods like Classical & Deep learning for image classification problems , and analyze the draw backs and how the other approach overcome the drawbacks and accuracy levels meet by these approaches over the years.

Computer Vision(CV), Convolution; Convolution Neural network(CNN) , Deep Learning ;Gradient , improvement in CV after CNN, Machine Learning, possible improvement in CV

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