Manifold Preserving CNN for Pixel-Based Object Labelling in Images for High Dimensional Feature spaces

Deep CNN’s have achieved an excellent performance in computer vision and image processing methods, designating them as a state-of-art in this domain. CNN based applications have achieved tremendous advancement towards vision computing with high dimensional object labelling in images. The complex nature of High Dimensional (HD) images limits the performance of CNN’s. In high dimensional feature space, the pixel-based image labelling is a complex problem for the parsing of objects in an image. To overcome this issue, we have studied a two-stage end-to-end framework that uses manifold embedding based patch-wise CNN architecture to extract the features and classify the image for labelled classes. We have investigated the deep-features with an information fusion technique for low dimensional feature space compression by using pre-trained CNNs and spatiality preserving manifold embedding in the first stage. The cost of pixel-based labelling in HD feature space is very high, so researchers have tried to encapsulate maximum information within the minimum image size. Therefore, in this stage, we have first increased the valuable information by concatenating the deep spatial features and then embedding the massive dataset by using manifold preservation. In stage-2, the image patches are extracted and passed into three layers of convolution-pooling pair and two layers of fully connected pair using parameter tuning. The training dataset is prepared in the form of pixel-label pairs. Subsequently, the proposed method has been evaluated on publicly available images and compared with the previously proposed schemes. The proposed method has outperformed the previous techniques in accuracy and computation time with a significant margin.