Color Object Detection Based Image Retrieval Using ROI Segmentation with Multi-Feature Method

Nowadays content-based image retrieval (CBIR) framework is drawing in consideration of numerous analysts because of far-reaching applications found in numerous territories. In this paper, a new CBIR methodology is proposed and adequacy of any CBIR framework relies upon the features extracted from a color picture. In this work, firstly find the region of interest of the image using Sobel and Canny method and later on output is applied on HSV color space, it is clear to human vision eye. For classification, neural network is used and categorized the data with class labels. The similarity distance is estimated between the query image and stored image with different similarity metrics like Manhattan distance, Euclidean distance, Chebyshev, Hamming distance and Jaccard distance. The experimental result is estimated on accuracy, precision. The experiment performed on two well-known databases i.e.: Corel-1k and Corel-5k dataset and new methodology proves the better accuracy results up to 87.33% and 68.93% respectively and improves the precision results also up to 86.36% and 68.47% respectively. In this paper, results are also extended up to 80%.