# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 2 Related Work  
According to the literature [6], SIMPLicity, Blobworld and Query by Image Content (QBIC) are examples of computer vision applications that rely on the extraction of visual features such as color, texture and shape. Image Rover and WebSeek are examples of image search systems that rely on a query-based or keyword-based image search [6]. The main objective of any CBIR system is to search for relevant images that are similar to the query image [22]. Overlapping objects, differences in the spatial layout of the image, changes in illumination and semantic gaps make CBIR challenging for the research community [8]. [PERSON] et al. [23] propose the Spatial Weighing BOF (SWBOF) model to extract the spatial information by using three approaches, i.e., local variance, local entropy and adjacent block distance. This model is based on the concept of the different parts of an image object contributing to image categorization in varying ways. The authors demonstrate significant improvement over the traditional methods. [PERSON] et al. [9] extract the visual information by dividing an image into triangular regions to capture the compositional attributes of an image. The division of the image into triangular cells is reported as an efficient method for histogram-based representation. [PERSON] et al. [24] propose spatiogram-based image representation that consists of a color histogram that is quantized by using Gaussian Mixture Models (GMMs). The quantized values of GMMs are used as an input for the learning of the Expectation-Maximization (EM). The retrieval is performed on the basis of the closeness of the feature vector values of two spatiograms which are obtained by using the Jensen-Shannon Divergence (JSD) [24]. [PERSON] et al. [25] investigate the impact of the integration of different mid-level features to enhance the performance of image retrieval. They investigate the impact of the integration of SIFT descriptors with LBP and HOG descriptors respectively, in order to address the problem of the semantic gap. Weighed \(k-\)means clustering is used for quantization, and best performance is reported with SIFT-LBP integration. To reduce the semantic gap between the low-level features and the high-level image concepts, [PERSON] et al. [8] propose image retrieval based on the visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded -Up Robust Features (SURF). Their approach acquires the strength of both features, i.e., invariance to scale and rotation of SIFT and robustness to illumination of SURF. In another recent work, [PERSON] et al. [26] propose a late fusion of binary and local descriptors i.e., FREAK and SIFT to enhance the performance of image retrieval. [PERSON] et al. [27] present an incremental and interactive localization and map-learning system based on BoW. [PERSON] et al. [28] propose a real-time assistive localization approach that extracts compact and effective omnidirectional image features which are then used to search a remote image feature-based database of a scene, in order to help indoor navigation. In another recent work, [PERSON] et al. [29] propose a hybrid framework of local (BoW) and global image features for efficient image retrieval. According to [PERSON] et al. [29], a multi-fusion based on two lines of image representation can enhance the performance of image retrieval. The authors [29] extract the texture information by using Intensity-Based Local Difference Patterns (ILDP) and by selecting the HSV color space. This scheme is selected to capture the spatial relationship patterns that exist in the images. The global color information is extracted by using the H and S components. The final feature vector is constituted by combining the H, S feature space and ILDP histograms. The experimental result validates that the fusion of color and texture information enhances the performance of image retrieval [29]. According to [PERSON] et al. [30], the ranking and incompatibility of the image feature descriptor is not considered much in the domain of image retrieval.