Một phương pháp mới chuẩn hoá dữ liệu và hiệu chỉnh trọng số cho tổ hợp đặc trưng trong tra cứu ảnh theo nội dung

Abstract

Relevance feedback as a bridge between high level semantic concepts and low features. It is important to improve the performance of content based image retrieval (CBIR) is preprocessing image features and refining distance measures for query based on user information needs. We propose a novel method to normilize features and distance for CBIR using combination features. In addition, we also use relevant feedback from users and learning from low features to update weights distance measures and refine query. Experimental results over the benchmark Corel dataset demonstrate the effectiveness of this propose method.