Content based image retrieval system by using HSV color histogram, discrete wavelet transform and edge histogram descriptor

In last few decades. Content Based Image Retrieval System (CBIR) is an emerging field to retrieve relevant images from a database. It utilizes the visual contents of an image for the local and global features. Local feature includes spatial domain which presents the significance of the image as well as the index of an image. Global feature includes shape descriptors, contour representations and texture features. Segmentation process is required in global feature extraction technique. It is a challenging task to simulate visual information in CBIR system. CBIR strategy combines the local and global features to deal with the low level information. In this paper, we proposed new CBIR technique to fuse color and texture features. Color Histogram (CH) is used to extract a color information. Texture features are extracted by Discrete Wavelet Transform (DWT) and Edge Histogram Descriptor (EDH). The features are created for each image and stored as a feature vector in the database. We evaluated our work using Corel 1-k dataset. To examine the accuracy with the other proposed systems, precision and recall methods are used that provides competitive and efficient result. The experimental results show that our proposed method outperforms with existing CBIR systems.