Abstract
In today’s digital era, several of the image retrieval systems focus on retrieving images using features from images themselves such as color, shape and textures and are referred as low-level features. In this proposed work, the features like color with HSV color space, color moments and Hu moments are employed to retrieve similar images. Various experimentations were conducted on Wang’s database images to test the combination of features for higher performance using precision, recall, accuracy and f-score. The results obtained are compared with one another and also with existing works. The retrieval performance is found to be high for proposed system against existing works.
Highlights
Content based image retrieval (CBIR) is a technique used in computer vision applications, using which the similar images from large databases are found and returned as a resultant set based on an input query image
To implement any CBIR system, there are of two phases namely, online and offline phases
We have used the features as HSV color histogram, color moments and the Hu’s invariant moments
Summary
Content based image retrieval (CBIR) is a technique used in computer vision applications, using which the similar images from large databases are found and returned as a resultant set based on an input query image. In order to overcome this issue, CBIR was introduced with feature extraction from the database images collected and use a similarity measure to compare the best matching images and return to the query system [1]. In this proposed work, we have used the standard ground truth dataset of Wang’s database images that comprises of 10 classes with 100 images in each class, making a total of 1000 images in the database [1, 5]. The feature vector for the entire database is collected, processed and stored as feature repository for future reference during querying of images
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