Abstract
An efficient non-uniform color quantization and similarity measurement methods are proposed to enhance the content-based image retrieval (CBIR) applications. The HSV color space is selected because it is close to human visual perception system, and a non-uniform color method is proposed to quantize an image into 37 colors. The marker histogram (MH) vector of size 296 values is generated by segmenting the quantized image into 8 regions (multiplication of 45°) and count the occurrences of the quantized colors in their particular angles. To cope with rotated images, an incremental displacement to the MH is applied 7 times. To find similar images, we proposed a new similarity measurement and other 4 existing metrics. A uniform color quantization of related work is implemented too and compared to our quantization method. One-hundred test images are selected from the Corel-1000 images database. Our experimental results conclude high retrieving precision ratios compared to other techniques.
Highlights
The increasing number on accessing the image database hosts by Internet users pay more attention to the researchers in developing different algorithms for the image retrieval problem, which is a branch of information retrieval
The Corel-1000 image database is selected in our experiments that consists of 10 categories (Africa, Beaches, Building, Buses, Dinosaurs, Elephants, Flowers, Horses, Mountains and Foods), and each category consists of 100 different true color images
Retrieved images are ranked according to their similarity values between the query image and the Corol-1000 image database, and four choices are selected to display the best 10, 15, 20, and 25 scores
Summary
The increasing number on accessing the image database hosts by Internet users pay more attention to the researchers in developing different algorithms for the image retrieval problem, which is a branch of information retrieval. The performance of the retrieval process is evaluated by the distance or similarity measurements between the query image and the image collection. The main process of the CBIR approaches is depicted, the features such as color, texture, shape, or combination are extracted from both the query image and the image database to be processed later, resulting into a marker vector (value or series of values) for each single image called marker histogram (MH). The advantages of using MHs are the ability to overcome the problem of depending on the color histogram that retrieves irrelevant images because of similarity in the histograms, and of dividing the image into 8 regions to find rotated images
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