In this paper, a new texture descriptor named "Fractional Local Neighborhood Intensity Pattern" (FLNIP) has been proposed for content based image retrieval (CBIR). It is an extension of the Local Neighborhood Intensity Pattern (LNIP)[1]. FLNIP calculates the relative intensity difference between a particular pixel and the center pixel of a 3×3 window by considering the relationship with adjacen...

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...

Local surface description is a critical stage for surface matching. This paper presents a highly distinctive local surface descriptor, namely TriSI. From a keypoint, we first construct a unique and repeatable local reference frame (LRF) using all the points lying on the local surface. We then generate three spin images from the three coordinate axes of the LRF. These spin images are concatenate...

In this paper we present a Content-Based Image Retrieval (CBIR) system which extracts color features using Dominant Color Correlogram Descriptor (DCCD) and shape features using Pyramid Histogram of Oriented Gradients (PHOG). The DCCD is a descriptor which extracts global and local color features, whereas the PHOG descriptor extracts spatial information of shape in the image. In order to evaluat...

Non-local means is a recently proposed denoising technique that better preserves image structures than other methods. However, the computational cost of non-local means is prohibitive, especially for large 3D images. Modifications have previously been proposed to reduce the cost, which result in image artefacts. This paper proposes a compact rotation invariant descriptor. Testing demonstrates i...

Face recognition is still a very demanding area of research. This problem becomes more challenging in unconstrained environment and in the presence of several variations like pose, illumination, expression, etc. Local descriptors are widely used for this task. The existing local descriptors are not able to utilize the wider local information to make the descriptor more discriminative. The wider...

This paper investigates possibilities for application of Kernel based Extreme Learning Machines (K-ELM) to the problem of multiclass image classification. It is combined with Local Binary Pattern (LBP) image descriptor, to reach highly accurate results. LBP is widely used global image descriptor characterized by compactness and robustness to illumination and resolution changes. Classification i...

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Color and texture data have been the primitive image or picture descriptors in substance or content based picture recovery frameworks. In this article, a technique is introduced for image or picture mining taking into account examination of shading...

We present an algorithm for extracting key-point descriptors using deep convolutional neural networks (CNN). Unlike many existing deep CNNs, our model computes local features around a given point in an image. We also present a face alignment algorithm based on regression using these local descriptors. The proposed method called Local Deep Descriptor Regression (LDDR) is able to localize face la...