Content based image retrieval using image features information fusion

Abstract

Recent image retrieval techniques are focusing on multiple image features for the efficient image retrieval. It has been an inevitable requirement to fetch the images from a variety of semantic groups and datasets. It is vital to retrieve the images based on their primitive features shape, texture, color and spatial information to cater the versatile image datasets. State-of-the-art detectors and descriptors are capable of finding the interest points based on their specialty. To encompass the strength of the image features for the information fusion purpose this contribution presents a novel technique to fuse the spatial color information with shaped extracted features and object recognition. For RGB channels L2 spatial color arrangements are applied and features are extracted, thereby fused with intensity ranged shapes formed by connecting the discovered edges and corners for the grey level image. Perifoveal receptive field estimation with 128-bit cascade matching with symmetric sampling on the detected interest points that discovers the potential information for the complex, overlay, foreground and background objects. Firstly the process is accomplished by reducing the massive features vectors, selecting high variance coefficient and secondly obtaining the indexing and retrieval by employing a Bag-of-Words approach. Extensive experiments are conducted on ten highly recognized image dataset benchmarks, specialized for texture, shapes, colors and objects including ImageNet, Caltech-256, Caltech-101, 102-Flower, Corel-10,000, 17-Flower, Corel-1000, COIL, ALOT and FTVL tropical fruits. To check the affectivity and robustness of the proposed method, it is compared with state-of-the-art detectors and descriptors SIFT, SURF, HOG, LBP, DoG, MSER and RGBLBP. Encouraging results reported that the proposed method has a remarkable performance in most of the image categories of versatile image datasets and can gain better precision to those of the state-of-the-art detectors and descriptors.