Abstract
The rapidly increasing number of digital images requires effective retrieval. Meanwhile, the dominant color descriptor has been widely used in image processing. Due to the influence of lighting and other factors, the same color in nature may have some different changes. The human eye is usually more sensitive to zones of consistent color, often identifying objects by zones of consistency. Therefore, the proposed method in this paper first applies the texton template to detect and extract the consistent zone of an image, and calculates the dominant color descriptor feature on the pixels in this consistent zone. Besides, the translation and rotation invariance of the Hu moments feature is applied to extract the shape information in the same consistent zone of the image. Finally, the combination of the dominant dolor descriptor and the Hu moments is used for content-based image retrieval. The algorithm proposed in this paper is tested on three data sets: Corel-1k, Corel-5k and Corel-10k, and the experimental results show that it is superior to the current content-based image retrieval methods.
Highlights
As mobile phones, video cameras, and other devices are capable of shooting and storing a large number of digital images, images on the Internet have shown a geometric progression
Based on the well-known bag of visual words (BovW) model, Ouni et al propose three different methodologies [11] inspired by the visual phrase model effectiveness and a compression technique which ensures the same effectiveness for retrieval than the BoVW model
A new method is proposed in this paper which combines the advantages of color and shape for image retrieval using the features of both dominant color descriptor (DCD) and Hu moments (DCD-HM)
Summary
Video cameras, and other devices are capable of shooting and storing a large number of digital images, images on the Internet have shown a geometric progression. The visual vocabulary based, SIFT feature-based, visual phrase-based, and deep feature-based image retrieval systems. The method in paper [10] represents a modified curvelet transform (MCT) and its combination with vocabulary tree (VT) for feature collection and retrieval of the images from the database. Due to the large-scale use of neural networks in the field of image classification and recognition, different types of deep neural networks are proposed for image retrieval. Kang et al propose a hashing scheme [13] for fast SIFT feature-based image matching and retrieval. The content-based image retrieval method handles the inherent characteristics of the image (color, shape, texture, etc.). A new method is proposed in this paper which combines the advantages of color and shape for image retrieval using the features of both DCD and Hu moments (DCD-HM). The features of DCD and Hu moments (DCD-HM) are extracted in this consistent zone to obtain better retrieval results
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