Object-based Classification of Natural Scenes Using Machine Learning Methods

Abstract

The replication of human intellectual processes by machines, particularly computer systems, is known as artificial intelligence (AI). AI is an intelligent tool that is utilized across sectors to improve decision making, increase productivity, and eliminate repetitive tasks. Machine learning (ML) is a key component of AI since it includes understanding and developing ways that can learn or improve performance on tasks. For the last decade, ML has been applied in computer vision (CV) applications. In computer vision, systems and computers extract meaningful data from digital videos, photos, and other visual sources and use that information to conduct actions or make suggestions. In this work, we have solved the image segmentation problem for the natural images to segment out water, land, and sky. Instead of applying image segmentation directly to the images, images are pre-processed, and statistical and textural features are then passed through a neural network for the pixel-wise semantic segmentation of the images. We chose the 5X5 window over the pixel-by-pixel technique since it requires less resources and time for training and testing.

Image Morphing Techniques: A Review

“…Thus, detecting forged images became a very important field of study in [45] the authors proposed a technique for detecting a single morphing image attack, using patterns and analyzing the principal component, which produced a good result. Good image segmentation algorithms may also be useful to classify the object or spaces in the image as explained in [46], that can help to detect forged image. So, the competition between the attacker and the detector is contiguous using different techniques including image morphing.…”