Content-Based Image Classification

Efficient Machine Learning Using Robust Feature Extraction Techniques

Rik Das

• Publisher: Chapman and Hall/CRC
• Year: 2020
• ISBN:
  9781000280715
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Vision & Pattern Recognition
• Index: Computer Science

Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques is a comprehensive guide to research with invaluable image data. Social Science Research Network has revealed that 65% of people are visual learners. Research data provided by Hyerle (2000) has clearly shown 90% of information in the human brain is visual. Thus, it is no wonder that visual information processing in the brain is 60, 000 times faster than text-based information (3M Corporation, 2001). Recently, we have witnessed a significant surge in conversing with images due to the popularity of social networking platforms. The other reason for embracing usage of image data is the mass availability of high-resolution cellphone cameras. Wide usage of image data in diversified application areas including medical science, media, sports, remote sensing, and so on, has spurred the need for further research in optimizing archival, maintenance, and retrieval of appropriate image content to leverage data-driven decision-making. This book demonstrates several techniques of image processing to represent image data in a desired format for information identification. It discusses 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:

• Image feature extraction with novel handcrafted techniques (traditional feature extraction)

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• Image feature extraction with automated techniques (representation learning with CNNs)

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• Significance of fusion-based approaches in enhancing classification accuracy

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• MATLAB® codes for implementing the techniques

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• Use of the Open Access data mining tool WEKA for multiple tasks

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The book is intended for budding researchers, technocrats, engineering students, and machine learning/deep learning enthusiasts who are willing to start their computer vision journey with content-based image recognition. The readers will get a clear picture of the essentials for transforming the image data into valuable means for insight generation. Readers will learn coding techniques necessary to propose novel mechanisms and disruptive approaches. The WEKA guide provided is beneficial for those uncomfortable coding for machine learning algorithms. The WEKA tool assists the learner in implementing machine learning algorithms with the click of a button. Thus, this book will be a stepping-stone for your machine learning journey.

Please visit the author's website for any further guidance at https://www.rikdas.com/

1

Introduction to Content-Based Image Classification

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1.1 Prelude

A picture collage contains an entire life span in a single frame. We have witnessed global excitement with pictorial expression exchanges compared to textual interaction. Multiple manifestation of social networking have innumerable image uploads every moment for information exchanges, status updates, business purposes and much more [1]. The cell phone industry was revolutionized with the advent of camera phones. These gadgets are capturing high-end photographs in no time and sharing the same for commercial and noncommercial usage [2]. Significant medical advancements have been achieved by Computer-Aided Diagnosis (CAD) of medical images [3]. Therefore, image data has become inevitable in all courses of modern civilization, including media, entertainment, tourism, sports, military services, geographical information systems, medical imaging and so on.

Contemporary advancement of computer vision has come a long way since its inception in 1960 [4,5]. Preliminary attempts were made for office automation tasks pertaining to approaches for pattern recognition systems with character matching. Research work by Roberts has envisaged the prerequisite of harmonizing two-dimensional features extracted from images to three-dimensional object representations [6]. Escalating complexities related to unevenly illuminated pictures, sensor noise, time, cost, etc. have raised realistic concerns for continuing the ensuing research work in the said domain with steadfastness and uniformity.

Radical advancements in imaging technology have flooded the masses with pictures and videos of every possible detail in their daily lives. Thus, the creation of gigantic image datasets becomes inevitable to store and archive all these rich information sources in the form of images. Researchers are facing mounting real-time challenges to store, archive, maintain, extract and access information out of this data [7].

Content-based image classification is identified as a noteworthy technique to handle these adversities. It has been considered effective to identify image data based on its content instead of superficial annotation.

Image annotation is carried out by labeling the image content with text keywords. It requires considerable human intervention to manually perform this action. Moreover, the probability of erroneous annotation is high in cases of labeling gigantic image datasets with a manual text entry procedure. The text tag describing image content is as good as the vocabulary of the person who tags it. Thus, the same image can have different descriptions based on the vocabulary of the annotation agent responsible for it, which in turn hampers the consistency of the entire process [8].

Conversely, extraction of a feature vector from the intrinsic pixels of the image data has eradicated the challenges faced due to manual annotation and has automated the process of identification with minimal human intervention [9]. Present day civilization follows the trend of capturing images of events and objects of unknown genre. The process of content-based image identification can readily classify the captured images into known categories with the help of preexisting training knowledge. This, in turn, assists in decision-making for further processing of the image data in terms of assorted commercial usage.

Promptness is not the only decisive factor for efficient image classification based on content. Accuracy of classification results contribute immensely to the success factor of a classification infrastructure. Thus, to ensure the competence of content-based image classification, one has to identify an effectual feature extraction technique. The extracted features become pivotal to govern the success rate of categorizing the image data into corresponding labels.

Therefore, different feature extraction techniques are discussed in this work to represent the image globally and locally by means of extracted features. The local approach functions on segmented image portions for feature extraction, contrary to the global approach. However, image data comprises a rich feature set, which is seldom addressed by a single-feature extraction technique. As a result, fusion of features has been explored to evaluate the classification results for improved accuracy.

The experiments are carried out on four widely used public datasets using four different classifiers to assess the robustness of extracted features. Diverse metrics, such as Precision, Recall, Misclassification Rate (MR) and F1 Score, are used to compare the classification results. A brief explanation of each of the metrics used is given in the following section. It is followed by the description of the classifiers and the datasets.

1.2 Metrics

Different parameters have been used to measure the classification performances of the diverse feature extraction techniques [10]. A brief explanation for each of the parameters is also provided.

1.2.1 Precision

Precision is defined as the fraction of appropriate classification among the classified instances as in equation 1.1

$Precision=\frac{TP}{TP+FP}$(1.1)

1.2.2 True Positive (TP) Rate/Recall

Recall is defined as the fraction of appropriate classification among the total number of rela...