의미적으로 중요한 시각적 내용의 온토로지 기반의 보안검색
Ontology-based Secure Retrieval of Semantically Significant Visual Contents
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초록
이미지 분류는 심이 높은 연구 분야로 이미지 데이터의 방한 양을 시각 콘텐츠에 따라 다양한 클래스로 분류 한다. 연구자들은 다른 카테고리에 이미지를 분류하기 한 다양한 하 수 특징 기반 기술을 제시하고 있다. 그 러나, 효율이고 효과인 분류 검색은 여히 시각인 내용의 복잡한 특성으로 인해 어려운 문제로 남아있다. 한, 기존의 정보검색기술은 보안에 취약하여, 환자의 기록 법 집행 기의 데이터베이스와 같은 개인의 시각 콘텐츠를 제3자에 의해 검색이 쉬웠다. 그러므로, 우리는 보안 이미지 분류 정보 검색에 한 이미지 스테가노그 래피를 사용하여 새로운 온톨로지 기반의 임 워크를 제안한다. 제안된 임워크는 하 계층 이미지 특징을 효율인 분류 결과인 온톨로지의 상 계층 컨셉의 매핑을 해 도메인 특징 온톨로지를 사용한다. 한 제안된 방 법은 내용 안에 비 메시지와 같은 이미지의 의미를 숨기고 정보 검색 로세스를 제3자로부터 보호하기 해 이미 지 스테가노그래피를 이용한다. 제안된 임워크는 기존기술의 복잡도를 최소화하고 개인의 이미지 데이터베이스 로부터 안하고 실시간 시각 콘텐츠 검색을 해 합한 기술을 증시킨다. 다른 최신 기술의 시스템과 비교하 여 실험한 결과 제안된 임 워크의 효율성, 유효성 보안을 확인 하다.
Abstract
Image classification is an enthusiastic research field where large amount of image data is classified into various classes based on their visual contents. Researchers have presented various low-level features-based techniques for classifying images into different categories. However, efficient and effective classification and retrieval is still a challenging problem due to complex nature of visual contents. In addition, the traditional information retrieval techniques are vulnerable to security risks, making it easy for attackers to retrieve personal visual contents such as patient’s records and law enforcement agencies’ databases. Therefore, we propose a novel ontology-based framework using image steganography for secure image classification and information retrieval. The proposed framework uses domain-specific ontology for mapping the low-level image features to high-level concepts of ontologies which consequently results in efficient classification. Furthermore, the proposed method utilizes image steganography for hiding the image’s semantics as a secret message inside them, making the information retrieval process secure from third parties. The proposed framework minimizes the computational complexity of traditional techniques, increasing its suitability for secure and real-time visual contents retrieval from personalized image databases. Experimental results confirm the efficiency, effectiveness, and security of the proposed framework as compared with other state-of-the-art systems.
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