Content-based image retrieval : survey

Abstract EN

Extensive use of digital photographic devices has resulted in large volumes of digital images being acquired and stored in databases.

Whether it is for scientific research, medical or social networking, there is a growing demand for effective retrieval of digital images based on their visual content (e.

g.

colour and texture).

Content-Based Image Retrieval systems are developed to meet this demand.

However, searching for similar and relevant images from large-scale databases still poses a challenge for Content-Based Image Retrieval systems due to the gap between high-level meaning and low-level visual features.

This paper reviews different Content-Based Image Retrieval approaches such as Clustering, Region-of-Interest, Bag-of-Visual-Words, Relevance Feedback, Browsing, and indexing that have been developed to reduce such “Semantic gap” issue.

So, the interested researchers can interest to determine which method is benefit to his work.

Extensive use of digital photographic devices has resulted in large volumes of digital images being acquired and stored in databases.

Whether it is for scientific research, medical or social networking, there is a growing demand for effective retrieval of digital images based on their visual content (e.

g.

colour and texture).

Content-Based Image Retrieval systems are developed to meet this demand.

However, searching for similar and relevant images from large-scale databases still poses a challenge for Content-Based Image Retrieval systems due to the gap between high-level meaning and low-level visual features.

This paper reviews different Content-Based Image Retrieval approaches such as Clustering, Region-of-Interest, Bag-of-Visual-Words, Relevance Feedback, Browsing, and indexing that have been developed to reduce such “Semantic gap” issue.

So, the interested researchers can interest to determine which method is benefit to his work.