Horizontal Max Pooling a Novel Approach for Noise Reduction in Max Pooling for Better Feature Detect

As of now CNN(Convolutional Neural Network) in DNN(Deep Neural Network) has become a main focus for better and faster feature detections. More and more deep neural networks have been proposed to improve the efficiency and accuracy of models. While all the models are based on the same building pillars like convolution subsampling, pooling, activations functions etc. used in the model. Till now many researchers have shown interest in using different activation functions like relu, leaky relu and as of now using softmax activation function but yet they all are using similar pooling approach “Max Pooling”. Using this we can detect the feature more prominently but also highlighting some unwanted features too referred to as noise. So in this paper we have introduced a new pooling approach “Horizontal Max Pooling” to reduce this noise for better feature detection and extraction. Although this paper contains a single image example we have tested this approach on many different images and matrices each result in noise reduction and visible and noticeable change.