Author Description

"Content-Based Image Classification Efficient Machine Learning using Robust Feature Extraction Techniques is a comprehensive guide to initiate and excel in researching with invaluable image data. Social Science Research Network has revealed the fact that sixty five percent of us are visual learners. Research data provided by Hyerle (2000) has clearly shown ninety percent of information in our brain is visual. Thus, it is no wonder that processing of visual information in brain is 60,000 times faster than text based information (3M Corporation, 2001). Recent times have witnessed significant surge in conversing with images with popularity of social networking platforms. The other reason of embracing extensive usage of image data is easy availability of image capturing devices in the form of high resolution cell phone cameras. Extensive application of image data in diversified application areas including, medical science, media, sports, remote sensing and so on has stimulated the requirement of further research in optimizing archival, maintenance and retrieval of appropriate image content to leverage data driven decision making. This book has demonstrated several techniques of image processing to represent image data in desired format for information identification. It has discussed the application of machine learning and deep learning for identifying and categorizing appropriate image data helpful in designing automated decision support systems. The book offers comprehensive coverage of the most essential topics, including: Different Open Access Image Datasets to start your Machine Learning Journey Image Feature Extraction with Novel Handcrafted Techniques (Traditional Feature Extraction) Image Feature Extraction with Automated Techniques (Representation Learning with CNNs) Significance of Fusion Based Approaches in enhancing Classification Accuracy Matlab Codes for implementing the Techniques Use of Open Access Data Mining tool Weka for multiple tasks The book is int