Abstract
The era in which we live has become the era of big data, particularly with the spread of the concept of Internet-of-Things (IoT) and artificial-intelligence (AI). Images constitute a large portion of this data that is created and handled. Image classification is the process of identifying and labeling groups of pixels or vectors inside an image using certain criteria. Major image classification techniques divided into two categories which are supervised and unsupervised techniques. This study looks at various research, techniques, and issues of image classification. The focus is on summarizing the main advanced classification strategies and methods such as (K-Mean clustering, Fuzzy measure, Artificial Neural Networks (ANN), Decision-Tree (DT), Support-Vector-Machines (SVMs), Naive Bayes (NB), K-Nearest-Neighbor (KNN), Random-Forest (RF), etc.) that can be utilized after some update to enhance classification accuracy. Also, this study shows the common challenges and solutions in this area.
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