Dominant and LBP-Based Content Image Retrieval Using Combination of Color, Shape and Texture Features

Content-based image retrieval based on color, texture and shape are important concepts that facilitate quick user interaction. Due to these reasons, humongous amount of explores in this direction has been done, and subsequently, current focus has now shifted in improving the retrieval precision of images. This paper proposes a dominant color and content-based image retrieval system using a blend of color, shape, and texture features. K-dominant color is extracted from the pixels finding and can be gathered in the form of cluster or color clusters for forming a cluster bins. The alike colors are fetched on the basis of distance calculations between the color combinations. Then the combination of hue, saturation, and brightness is calculated where hue shows the exact color, and the color purity is shown by saturation, and the brightness of the percentage degree increases from black to white. Experimental results clearly indicate that the proposed method outperforms the existing state of the art like LBP, CM, and LBP and CM in combination.