GEO matching regions: multiple regions of interests using content based image retrieval based on relative locations

• Published in: Multimedia tools and applications 2017-07, Vol.76 (14), p.15377-15411
• Main Authors: Memon, Muhammad Hammad, Li, Jian-Ping, Memon, Imran, Arain, Qasim Ali
• Format: Article
• Language: English
• Subjects: Annotations; Audio data; Clustering; Color; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Feature extraction; Image contrast; Image management; Image retrieval; Information retrieval; Multimedia; Multimedia Information Systems; Object recognition; Outliers (statistics); Principal components analysis; Shape recognition; Special Purpose and Application-Based Systems; System effectiveness
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-016-3834-z

Information retrieval systems are getting more attention in the era of multimedia technologies such as an image, video, audio and text files. The large numbers of images are challenges in computer systems field to store, manage data effectively and efficiently. The shape retrieval feature of different objects in the image also remains a difficult problem due to distinct angle view of different objects in a scene only; few studies have reported solution to the problem of finding relative locations of ROIs. In this paper, we proposed three methods such as1. Geolocation-based image retrieval (GLBIR), 2.Unsupervised feature technique Principal component analysis (PCA) and 3.multiple region-based image retrieval. The first proposed (GLBIR) method identifies geo location an image using visual attention based mechanism and its color layout descriptors. These features are extracted from geo-location of query image from Flickr database. Our proposed model does not fully semantic understanding of image content, uses visual metrics for example; the proximity ,color contrast, size and nearness to image’s boundaries to locate viewer’s attention. We analyzed results and compared with state of art CBIR Systems and GLBIR Technique. Our second method to refine images exploiting and fusing by unsupervised feature technique using principal component analysis (PCA). The visually similar images clustering together with analyses image retrieval process and remove outliers initially retrieved image set by PCA. To evaluation our proposed approach, we used thousands of images downloaded from Flickr and CIFAR-10 databases using Flickr public API. Finally, we determinately proposed a system for image retrieval based on region. It provides a user interface for availing to designate the watershed ROI within an input image. During the retrieval of images, regions’ feature vectors having codes of region homogeneous to a region of input image are utilized for comparison. Standard datasets are used for evaluation of proposed approach. The experiment demonstrates and effectiveness of the proposed method to achieve higher annotation performance increases accuracy and reduces image retrieval time. We evaluated our proposed approach on images dataset from Flickr and CIFAR-10.