Abstract
For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.
Highlights
Due to the rapid growth of the internet and advancements in image acquisition devices, increasing amounts of visual data are created and stored, leading to an exponential increase in the volume of image collections
The main objective of this paper is to present a novel technique based on visual words fusion or integration as well as the features fusion of speeded-up robust features (SURF)-fast retina keypoint (FREAK) feature descriptors on the basis of the bag-of-visual-words (BoVW) model in order to reduce the issue of the semantic gap and to improve the image retrieval performance
The dictionary is constructed using all of the images from the training set and performance is tested by taking images from the testing set of each image collection
Summary
Due to the rapid growth of the internet and advancements in image acquisition devices, increasing amounts of visual data are created and stored, leading to an exponential increase in the volume of image collections. The techniques have been introduced to improve the effectiveness as well as efficiency of the content-based image retrieval (CBIR) systems [1,2,3,4,5]. CBIR-IVR the visual contents of the query image These image retrieval techniques are based on either query by text or query by example. Traditional annotation-based image retrieval techniques are language-dependent. To resolve such issues, researchers focus on retrieving images on the basis of the visual contents of the images. The challenges in the design of CBIR systems are bridging the spatial layout, overlapping objects, variations in illuminations, semantic gap, rotation and scale changes in images, and exponential growth of the image collections [6,7,8,9,10,11]
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