An Efficient Algorithm for Facial Image Classification

다국어 초록 (Multilingual Abstract)

Image mining is one of the data mining research areas and it can be defined as getting hidden information from the image databases. It is used to identify unknown patterns, inherent and valuable information from images. Image mining helps to make relationships between various categories of images which are found in large image databases. These images can reveal useful information to the users. Image mining is distinct from low-level computer vision and image processing techniques. It uses methods from computer vision, image retrieval, image processing, data mining, database, machine learning, and artificial intelligence. Although all these subjects study the same object image, the vital difference between image data mining and the other subjects is, image data mining focuses on large scale set of images while image processing and pattern recognition analysis are based on only single image. Face detection is the problem of determining whether a sub-window of an image contains a face. It has received much attention and has been an extensive research topic in recent years. In this research work, facial images are classified based on its shape feature using optimization algorithms. A new algorithm, i.e. classification based similarity finding is proposed for classifying the facial images as round or oval shape. The performance of the proposed classification based similarity algorithm is compared with the particle swam optimization and genetic algorithms. The results of the existing and proposed algorithms are analyzed based on accuracy and execution time factors. From this we observed that the proposed classification based similarity finding algorithm has produced good results.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Related Works
• 3. Proposed Work
• 3.1. Preprocessing
• 3.2. Segmentation
• 3.3. Edge Detection
• 3.4. Classification
• 3.5. Particle Swarm Optimization
• 3.6. Genetic Algorithm
• 3.7. Selection Operator
• 3.8. Crossover
• 3.9. Mutation
• 3.10. Classification by Similarity Finding
• 4. Experimental Results
• 5. Conclusion
• References