Probabilistic models with hidden variables such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have recently become popular for solving several image content analysis tasks. In this work we will use a pLSA model to represent images for performing scene classification. We evaluate the influence of the type of local feature descriptor in this context and co...

Pattern clustering is an important data analysis process useful in a wide spectrum of computer vision applications. In addition to choosing the appropriate clustering methods, particular attention should be paid to the choice of the features describing patterns in order to improve the clustering performance. This paper presents a novel feature descriptor, referred as Histogram of Structure Tens...

Shapes and texture image recognition usage is an essential branch of pattern recognition. It is made up of techniques that aim at extracting information from images via human knowledge and works. Local Binary Pattern (LBP) ensures encoding global and local information and scaling invariance by introducing a look-up table to reflect the uniformity structure of an object. However, edge direction ...

This paper presents a novel LDP based image descriptor which is more robust to temporal face changes. LDP is a framework to encode directional pattern based on local derivative variations, hence LDP is highly directional. However texture based features extracted globally tend to average over the image area. Hence this paper proposes to divide the face image into multiple regions and perform LDP...

This paper presents a method called Robust Edge Aware Descriptor (READ) to compute local gradient information. The proposed method measures the similarity of the underlying structure to an edge using the 1D Fourier transform on a set of points located on a circle around a pixel. It is shown that the magnitude and the phase of READ can well represent the magnitude and orientation of the local gr...

Fourier Coefficients have long been used to achieve invariance to signal transformations. In this paper we propose a Global Rotation Invariant Descriptor (GRID) for matching images based on the Discrete Fourier Transform (DFT). Following the analysis of the sampling criteria for generating such descriptors, it is shown that the descriptor can be made invariant to changes in scale as well under ...

In this paper, we proposed a system of automatically classifying different types of dates from their images. Different dates have various distinguished features that can be useful to recognize a particular date. These features include color, texture, and shape. In the proposed system, a color image of a date is decomposed into its color components. Then, local texture descriptor in the form of ...

Scale and affine-invariant local features have shown excellent performance in image matching, object and texture recognition. This paper optimizes keypoint detection to achieve stable local descriptors, and therefore, an improved image representation. The technique performs scale selection based on a region descriptor, here SIFT, and chooses regions for which this descriptor is maximally stable...