# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 1 Introduction  
In recent years, with the rapid development of imaging technology, searching or retrieving a relevant image from an image archive has been considered an open research problem for computer vision based applications [1; 2; 3; 4]. Higher retrieval accuracy, low memory usage and reduction of semantic gap are examples of common problems related to multimedia analysis and image retrieval [3; 5]. The common applications of multimedia and image retrieval are found in the fields of video surveillance, remote sensing, art collection, crime detection, medical image processing and image retrieval in real-time applications [6]. Most of the retrieval systems, both for multimedia and images, rely on the matchingof textual data with the desired query [6]. Due to the existing semantic gaps, the performance of these systems suffers [7]. The appearance of a similar view in images belonging to different image categories, results in the closeness of the feature vector values, and degrades the performance of image retrieval [6]. The main focus of the research in Content-Based Image Retrieval (CBIR) is to retrieve images that are in a semantic relationship with a query image [8]. CBIR provides a framework that compares the visual feature vector of a query image to the images places in the dataset [9]. The Bag of Visual Words (BoVW), also known as Bag of Features (BoF) [10], is commonly used for video and image retrieval [11]. The local features or interest point detectors are extracted from a group of training images. To achieve a compact representation, the feature space is quantized to construct a code-book that is also known as visual vocabulary or visual dictionary. The final feature vector, which consists of histograms of visual words, is orderless with respect to the sequence of co-occurrences in the 2D image space. The performance of the BoVW model suffers as the extraction of spatial information is beneficial in image classification and retrieval-based problems [6; 12]. Various approaches have been proposed to enhance the performance of image retrieval, such as soft assignments, computation of larger codebooks and visual word fusion [8]. All of these techniques do not contain any information about the visual word's locations in the final histogram-based representation [13]. There are two common techniques that can compute the spatial information from the image. These are based on (1) the construction of histograms from different sub-regions of image, and (2) visual word co-occurrence [13; 14; 15]. The first approach is to split the image into different cells for the histogram's computation; it is reported to be robust for content-based image matching applications [16]. Spatial Pyramid Matching (SPM) [16] is considered as a notable contribution for the computation of spatial information for BoVW-based image representation. In SPM, an image is divided into different sizes of rectangular regions for the creation of level-0, level-1 and level-2 histograms of visual words. However, SPM is sensitive to image transformations (i.e., rotation, flipping and translation) and loses its discriminative power, resulting in the misclassification of two similar scene images [17; 18]. The second approach to the computation of spatial layout is based on relationships among visual words [19; 20; 21]. This paper proposes a novel approach to extracting the image spatial layout based on global relative spatial orientation of visual words. This is achieved by computing the angle between identical visual word pairs with respect to the centroid in the image. Figure 1 provides an illustration to better understand the proposed approach. The image in Figure 1 is rotated at varying angles. It can be seen that the same angle is computed between visual words irrespective of the image orientation. The main contributions of this research are the following: (1) the addition of the discriminating relative global spatial information to the histogram of BoVW model and (2) reduction of the semantic gap.