Method and system for deep learning based image feature extraction

Abstract

The present disclosure provides a model for deep learning based image feature extraction considering a range of useful negative features. Conventional methods are either considering only positive features or considering all negative features along with positive features which leads to bias in feature extraction. The present disclosure overcomes the problem of the conventional methods using a bounded Rectified Linear activation Unit (B-ReLU) activation function based Bounded-Rectifier Network (B-RectNet). Initially, the present disclosure receives an image pertaining to an object. Further, the received image is preprocessed to remove a plurality of anomalies associated with the image a preprocessing technique. Further, a plurality of image features are extracted based on the preprocessed image using a trained B-RectNet. The bounded ReLU activation function filters a plurality of negative features based on a lower negative bound value and an upper negative bound value before inputting a plurality of feature values to a subsequent layer.