We present an experimental comparison of 3D feature descriptors with application to threat detection in Computed Tomography (CT) airport baggage imagery. The detectors range in complexity from a basic local density descriptor, through local region histograms and three-dimensional (3D) extensions to both to the RIFT descriptor and the seminal SIFT feature descriptor. We show that, in the complex...

It is well recognized that image representation is the most fundamental task of the face recognition, effective and efficient image feature extraction not only has small intraclass variations and large interclass similarity but also robust to the impact of pose, illumination, expression and occlusion. This paper proposes a new local image descriptor for face recognition, named Log–Gabor Weber d...

This paper presents several novel Gabor-based color descriptors for object and scene image classification. Firstly, a new Gabor-HOG descriptor is proposed for image feature extraction. Secondly, the Gabor-LBP descriptor derived by concatenating the Local Binary Patterns (LBP) histograms of all the component images produced by applying Gabor filters is integrated with the Gabor-HOG using an opti...

Four novel color Local Binary Pattern (LBP) descriptors are presented in this chapter for scene image and image texture classification with applications to image search and retrieval. Specifically, the first color LBP descriptor, the oRGB-LBP descriptor, is derived by concatenating the LBP features of the component images in an opponent color space — the oRGB color space. The other three color ...

This paper presents a texture descriptor for color texture classification specially designed to be robust against changes in the illumination conditions. The descriptor combines a histogram of local binary patterns (LBPs) with a novel feature measuring the distribution of local color contrast. The proposed descriptor is invariant with respect to rotations and translations of the image plane and...

In the field of machine learning, which values / data labeling or recognition is done by pattern recognition. Visual object classification is an example of pattern recognition, which attempts prompt to assign each object to one of a given set of object classes. The basic elements of the process of pattern recognition, feature extraction, feature selection and classification. The complexity of f...

Image local scale invariant features are of great importance for object recognition. Among various local scale invariant feature descriptors, Scale Invariant Feature Transform (SIFT) descriptor has been shown to be the most descriptive one and thus widely applied to image retrieval, object recognition and computer vision. By SIFT descriptor, an image may be described by hundreds of key points w...

A new method to describe texture images using a hybrid combination of local and global texture descriptors is proposed in this paper. In this regard, a new adaptive local binary pattern (ALBP) descriptor is presented in order to carry out the local description. It is built by adding oriented standard deviation information to an ALBP descriptor in order to achieve a more complete representation ...

Recently spatial pyramid matching (SPM) with scale invariant feature transform (SIFT) descriptor has been successfully used in image classification. Unfortunately, the codebook generation and feature quantization procedures using SIFT feature have the high complexity both in time and space. To address this problem, in this paper, we propose an approach which combines local binary patterns (LBP)...