Abstract
Image data has turned out to be a significant means of expression with the advancements of digital image processing technologies. Image capturing devices has now transformed to commodities due to smart integration with cell phones and other useful devices. Huge amount of images are getting accumulated daily in gigantic databases which requires categorization for prompt retrieval in real time. Content based image classification (CBIC) thus gained it's popularity in classifying images to their corresponding categories. Feature extraction techniques are the foundation of CBIC to represent the image data in the form of feature vectors. This work has implemented three different feature extraction techniques from spatial domain, transform domain and deep learning domain. The three different feature vectors feature vector are contrasted to investigate the robustness of descriptor definition for content based image classification
Highlights
Classification of images based on content of the image data has revealed remarkable growth in recent times due to improved techniques of feature extraction and robust machine learning algorithms
This work has carried out feature extraction using all the three techniques, namely, binarization, image transformation and pretrained CNN based feature extraction
The following section has described each of the techniques in brief initiating with the binarization technique followed by image transform technique and automated feature extraction using pretrained CNN technique
Summary
Classification of images based on content of the image data has revealed remarkable growth in recent times due to improved techniques of feature extraction and robust machine learning algorithms. Prior to automated feature extraction using deep neural networks, traditional techniques of handcrafted feature extraction has contributed immensely towards progressive development of content based image classification [2]. Feature engineering has a major role in formulating robust content based features using hand crafted techniques [3]. Feature extraction using deep learning techniques hardly require any manual intervention for revealing credible feature patterns from content based image data. Two of the techniques are handcrafted ones and the last one is based on pretrained convolutional neural network [4]. Feature extraction using handcrafted techniques embrace two popular methods, namely, Revised Manuscript Received on February 05, 2020 * Correspondence Author
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