Phân loại cho các hàm mật độ xác suất và ứng dụng cho ảnh
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Abstract
This study proposes a classification algorithm for probability density functions (PDFs) which is then applied to image data. The proposed algorithm is detailed step-by-step and illustrated using a specific set of PDFs. For application to image data, the study extracts color features using four basic colors represented as one-dimensional PDFs. Next, the method for finding prior probabilities based on fuzzy cluster analysis techniques is built. Finally, the quasi-Bayesian classification principle is established. Application on a specific set of images shows good classification results and demonstrates significant potential for practical applications across various fields.
Tóm tắt
Nghiên cứu này đề xuất một thuật toán phân loại cho các hàm mật độ xác suất (PDF) để từ đó áp dụng cho dữ liệu ảnh. Thuật toán đề nghị được trình bày chi tiết các bước thực hiện và được minh hoạ trên một tập PDF cụ thể. Để áp dụng cho dữ liệu ảnh, nghiên cứu trích xuất đặc trưng màu sắc với 4 màu cơ bản thành các PDF một chiều đại diện. Sau đó, phương pháp tìm xác suất tiên nghiệm dựa trên kỹ thuật phân tích chùm mờ được xây dựng. Cuối cùng, nguyên tắc phân loại tựa Bayes được thiết lập. Ứng dụng trên tập ảnh cụ thể cho thấy kết quả phân loại tốt và có nhiều tiềm năng trong áp dụng thực tế của nhiều lĩnh vực khác nhau.
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