Saadat Hanif Dar

TL;DR: A comprehensive review of the recent development in the area of CBIR and image representation is presented and the main aspects of various image retrieval and image representations models from low-level feature extraction to recent semantic deep-learning approaches are analyzed.

TL;DR: A detailed study about various computer vision-based approaches with application in textile industry to detect fabric defects and the drawbacks and limitations associated with the existing published research are discussed.

TL;DR: A novel image representation method is proposed where the algorithm is trained for classifying medical images by deep learning technique and the results exhibit that the method is best suited to classify various medical images for various body organs.

TL;DR: A novel approach to encoding the relative spatial information for histogram-based representation of the BoVW model is introduced by computing the global geometric relationship between pairs of identical visual words with respect to the centroid of an image.

TL;DR: A deep convolutional neural network (dCNN) using augmented face dataset to extract discriminative features from face images containing synthetic makeup variations to compete with the state of the art.