Akrem El-ghazal

Farthest point distance: A new shape signature for Fourier descriptors

Shape description is an important task in content-based image retrieval (CBIR). A variety of techniques have been reported in the literature that aims to represent objects based on their shapes. Each of these techniques has its pros and cons. Fourier descriptor (FD) is one of these techniques a simple, yet powerful technique that offers attractive properties such as rotational, scale, and translational invariance. Shape signatures, which constitute an essential component of Fourier descriptors, reduce 2-D shapes to 1-D functions and hence facilitate the process of deriving invariant shape features using the Fourier transform. A good number of shape signatures have been reported in the literature. These shape signatures lack important shape information, such as corners, in their representations. This information plays a major role in distinguishing between different shapes. In this paper, we present the farthest point distance (FPD), a novel shape signature that includes corner information to enhance the performance of shape retrieval using Fourier descriptors. The signature is calculated at each point on a shape contour. This signature yields distances calculated between the different shape corners, and captures points within the shape at which the human focuses visual attention in order to classify shapes. To reach a comprehensive conclusion about the merit of the proposed signature, the signature is compared against eight popular signatures using the well-known MPEG-7 database. Furthermore, the proposed signature is evaluated against standard boundary- and region-based techniques: the curvature scale space (CSS) and the Zernike moments (ZM). The FPD signature has demonstrated superior overall performance compared with the other eight signatures and the two standard techniques.

A New Shape Signature for Fourier Descriptors

Shape-based image description is an important approach to content-based image retrieval (CBIR). A variety of techniques are reported in the literature that aim to represent objects based on their shapes; each of these techniques has its advantages and disadvantages. Fourier descriptor (FD), a simple yet powerful technique, has attractive properties such as rotational, scale, and translational invariance. In this paper we investigate this technique and present a novel shape registration method for extracting Fourier descriptors. When evaluated against curvature scale space (CSS) and Zernike moments (ZM) in shape-based image retrieval, the proposed technique exhibits superior performance.

Invariant curvature-based Fourier shape descriptors

Shape descriptors have demonstrated encouraging potential for retrieving images based on image content, and a number of them have been reported in the literature. Nevertheless, most of the reported descriptors are still face accuracy and computational challenges. Fourier descriptors are considered to be promising descriptors as they are based on a sound theoretical foundation and also have the advantages of computational efficiency and attractive invariance properties. This paper proposes a new curvature-based Fourier descriptor (CBFD) for shape retrieval. The proposed descriptor takes an unconventional view of the curvature-scale-space representation of a shape contour as it treats it as a 2-D binary image (hence referred to as curvature-scale image, or CSI). The invariant descriptor is derived from the 2-D Fourier transform of the curvature-scale image. This method allows the descriptor to capture the detailed dynamics of the shape curvature and enhance the efficiency of the shape-matching process. Experiments using the widely known MPEG-7 databases in conjunction with a created noisy database have been conducted in order to compare the performance of the proposed descriptor with six commonly used shape-retrieval descriptors: curvature-scale-space descriptor (CSSD), angular radial transform descriptors (ARTD), Zernike moment descriptors (ZMD), radial Tchebichef moment descriptors (RTMD), generic Fourier descriptor (GFD), and the 1-D Fourier descriptor (1-FD). The performance of the proposed descriptor has surpassed that of many of these notable descriptors.

Shape-based image retrieval using pair-wise candidate co-ranking

Shape-based image retrieval is one of the most challenging aspects in Content-Based Image Retrieval (CBIR). A variety of techniques are reported in the literature that aim to retrieve objects based on their shapes; each of these techniques has its advantages and disadvantages. In this paper, we propose a novel scheme that exploits complementary benefits of several shape-based image retrieval techniques and integrates their assessments based on a pairwise co-ranking process. The proposed scheme can handle any number of CBIR techniques; however, three common techniques are used in this study: Invariant Zernike Moments (IZM), Multi-Triangular Area Representation (MTAR), and Fourier Descriptor (FD). The performance of the proposed scheme is compared with that of each of the selected techniques. As will be demonstrated in this paper, the proposed co-ranking scheme exhibits superior performance.

A novel curvature-based shape Fourier Descriptor

Shape descriptors have demonstrated encouraging potential in retrieving images based on image content. A number of shape descriptors have been reported in the literature. Nevertheless, most of the reported descriptors still face accuracy and computational challenges. Fourier descriptors are considered to be promising descriptors as they are based on sound theoretical foundation, and possess computational efficiency and attractive invariance properties. In this paper, we propose a novel Fourier descriptor based on contour curvature. The proposed descriptor takes an unconventional view of the curvature-scale-space representation of a shape contour as it treats it as a 2-D binary image (hence referred to as Curvature-Scale Image, or CSI). The invariant descriptor is derived from the 2-D Fourier transform of the Curvature-Scale Image. This allows the descriptor to capture detailed dynamics of the shape curvature and enhance the efficiency of the shape matching process. Experiments using images from the MPEG-7 database have been conducted to compare the performance of the proposed descriptor with the Curvature- Scale-Space Descriptor (CSSD), the Generic Fourier Descriptor (GFD), and the 1-D Fourier Descriptor (1-FD). The proposed descriptor demonstrated superior performance.

Scale Invariants of Radial Tchebichef Moments for Shape-Based Image Retrieval

Region-based descriptors often use moments to describe shapes. Recently, the discreet radial Tchebichef moment descriptors have been proposed. The radial Tchebichef moments are invariant with respect to image rotation. In order to achieve the scale invariance, researchers resort to resizing the original shape to predetermined size. This traditional scheme of scaling is time expensive and leads to the loss of some characteristics of a shape. Therefore, moments derived using the traditional normalization scheme may differ from the true moments of the original shape. In this paper, a simple yet powerful scheme has been proposed to derive a new set of scale invariants of radial Tchebichef moments. This scheme uses the area and the maximum radial distance of a shape to normalize the radial Tchebichef moments. The MPEF-7 scale-invariant database is used to evaluate the performance of the proposed scheme against four commonly used shape descriptors.

A consensus-based fusion algorithm in shape-based image retrieval

Shape-based image retrieval techniques are among the most successful content-based image retrieval (CBIR) approaches. In recent years, the number of shape-based image retrieval techniques has dramatically increased; however, each technique has both advantages and shortcomings. This paper proposes a consensus-based fusion algorithm to integrate several shape-based image retrieval techniques so as to enhance the performance of the image retrieval process. In this algorithm, several techniques work as a team: they exchange their ranking information based on pair-wise co-ranking to reach a consensus that will improve their final ranking decisions. Although the proposed algorithm handles any number of CBIR techniques, only three common techniques are used to demonstrate its effectiveness. Several experiments were conducted on the widely used MPEG-7 database. The results indicate that the proposed fusion algorithm significantly improves the retrieval process.