Abstract
A large number of growing digital images require retrieval effectively, but the trade-off between accuracy and speed is a tricky problem. This paperwork proposes a lightweight and efficient image retrieval approach by combining region and orientation correlation descriptors (CROCD). The region color correlation pattern and orientation color correlation pattern are extracted by the region descriptor and the orientation descriptor, respectively. The feature vector of the image is extracted from the two correlation patterns. The proposed algorithm has the advantages of statistic and texture description methods, and it can represent the spatial correlation of color and texture. The feature vector has only 80 dimensions for full color images specifically. Therefore, it is very efficient in image retrieving. The proposed algorithm is extensively tested on three datasets in terms of precision and recall. The experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.
Highlights
The rapid and massive growth of digital images requires effective retrieval methods, which motivates people to research and develop effective image storage, indexing, and retrieval technologies [1,2,3,4]
The orientation color correlation pattern is obtained by the orientation descriptor, and the color correlation histogram of the four orientations is obtained by statistics of the correlation pattern
The performance of an image retrieval system is normally measured using precision PT and recall PR for retrieving top T images defined by formula (9) and (10), respectively, where n is the number of relevant images retrieved from top T positions and R is the total
Summary
The rapid and massive growth of digital images requires effective retrieval methods, which motivates people to research and develop effective image storage, indexing, and retrieval technologies [1,2,3,4]. Image retrieval and indexing have been applied in many fields, such as the internet, media, advertising, art, architecture, education, medical, biological, and other industries. The text-based image retrieval process first manually labels the image with text and uses keywords to retrieve the image. This method of retrieving an image based on the degree of character matching in the image description is time-consuming and subjective. According to the working principle of general image retrieval, there are three keys to content-based image retrieval: selecting appropriate image features, adopting effective feature extraction methods, and accurate feature matching strategies
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