Image Pattern Recognition
Fundamentals and Applications
- Available for pre-order. Item will ship after October 13, 2021
This book describes various types of image patterns for image retrieval. All these patterns are texture dependent. Few image patterns such as Improved directional local extrema patterns, Local Quantized Extrema Patterns, Local Color Oppugnant Quantized Extrema Patterns and Local Mesh quantized extrema patterns are presented. Inter-relationships among the pixels of an image are used for feature extraction. In contrast to the existing patterns these patterns focus on local neighborhood of pixels to creates the feature vector. Evaluation metrics such as precision and recall are calculated after testing with standard databases i.e., Corel-1k, Corel-5k and MIT VisTex database. This book serves as a practical guide for students and researchers.
-The text introduces two models of Directional local extrema patterns viz.,
- Integration of color and directional local extrema patterns
- Integration of Gabor features and directional local extrema patterns.
-Provides a framework to extract the features using quantization method
-Discusses the local quantized extrema collected from two oppugnant color planes
-Illustrates the mesh structure with the pixels at alternate positions.
Table of Contents
1. INTRODUCTION 1.1 Data Retrieval 1.2 Content Based Image retrieval system 1.3 Organization of Book 2. FEATURES USED FOR IMAGE RETRIEVAL 2.1 Introduction 2.2 Color features 2.3 Texture features 2.4 Local features 2.5 Shape features 2.6 Multiple features 2.7 Problem statement 2.8 Methodology 3. IMPROVED DIRECTIONAL LOCAL EXTREMA
PATTERNS FOR CBIR 3.1 Introduction 3.2 Local patterns 3.3 Directional local extrema Patterns 3.4 Improved directional local extrema patterns 3.5 Conclusion 4. LOCAL QUANTIZED EXTREMA PATTERNS 4.1 Introduction 4.2 Local quantized extrema patterns 4.3 Experimental results and discussion 4.4 Conclusions 5. LOCAL COLOR OPPUGNANT QUANTIZED EXTREMA PATTERNS 5.1 Introduction 5.2 Local color oppugnant quantized extrema patterns 5.3 Experimental results and discussion 5.4 Conclusions 6. LOCAL MESH QUANTIZED EXTREMA PATTERNS 6.1 Introduction 6.2 Local mesh quantized extrema patterns 6.3 Experimental results and discussion 6.4 Conclusions 7. LOCAL PATTERNS FOR FEATURE EXTRACTION 7.1 Quantized Neighborhood Local Intensity Extrema Patterns For Image Retrieval 7.1.1 Major advantages over other methods 7.1.2 Framework of proposed retrieval system 7.1.3 Image Similarity measurement 7.1.4 Experimental results and discussion 7.1.5 Conclusion 7.2 Magnitude Directional Local Extrema Patterns 7.2.1 Introduction 7.2.2 Different types of local patterns 7.2.3 Proposed CMDLEP System 7.2.4 Experimental Results 7.2.5 Conclusion 7.3 Combination of CDLEP and Gabor Features 7.3.1 Introduction 7.3.2 Local Patterns and Variations 7.3.3 Proposed Gabor CDLEP System 7.3.4 Experimental Results 7.3.5 Conclusion 7.4 LEMP: A Robust Image Feature Descriptor for Retrieval Applications 7.4.1 Introduction 7.4.2 Related Local Patterns 7.4.3 Proposed Framework 7.4.4 Conclusion 7.5 Multiple Color Channel Local Extrema Patterns for Image Retrieval 7.5.1 Introduction 7.5.2 Relevant Work 7.5.3 Proposed Method 7.5.4 Experimental Results and Discussions 7.5.5 Conclusion and future scope 7.6 Integration of MDLEP and Gabor Function as a Feature Vector for Image Retrieval System 7.6.1 Introduction 7.6.2 Local Patterns & Variations 7.6.3 Proposed CMDLEP System 7.6.4 Experimental Results 7.6.5 Conclusions 7.7 Local Co-occurrence Patterns 7.7.1 Introduction 7.7.2 Local Patterns 7.7.3 Framework of the proposed system 7.7.4 Experimental Results and Discussions 7.7.5 Conclusion 7.8 Color Based Multi-Directional Local MOTIF XOR Patterns for Image Retrieval 7.8.1 Introduction 7.8.2 Feature Extraction Methods 7.8.3 Proposed Feature Descriptors 7.8.4 Experimental Results and Discussions 7.8.5 Conclusion 7.9 Quantized Local Trio Patterns for Multimedia Image Retrieval System. 7.9.1 Introduction 7.9.2 A Review of local patterns 7.9.3 Proposed Method 7.9.4 Experimental Results and Discussions 7.9.5 Conclusion 8. CONCLUSIONS AND FUTURE SCOPE 8.1 Summary 8.2 Salient features 8.3 Future scope REFERENCES
L Koteswara Rao is currently working as a professor, department of electronics and communication engineering, K L University, Telangana, India. He has more than 18 years of teaching and research experience. He has published more than 30 papers in various reputed national, international journals and conferences. His research interests include image processing, signal processing, embedded systems, and the Internet of Things (IoT). Md Zia Ur Rahman is presently working as a professor, department of electronics and communication engineering, K L University, Andhra Pradesh, India. His current research interests include adaptive signal processing, biomedical signal processing, medical imaging, array signal processing, MEMS, Nanophotonics. He has published more than 100 research papers in various journals and proceedings and authored 2 books. He is serving in various editorial boards in the capacity of editor in chief, associate editor, reviewer for publishers like IEEE, Elsevier, Springer, American Scientific Publishers, etc. P Rohini is currently working as an assistant professor, department o computer science engineering, ICFAI University, Hyderabad, India. She has 14 years of teaching experience. Her research interests include image processing, data mining, and deep learning.