A challenging problem of computer vision is scene classification. An efficient method for classifying natural scenes from the Oliva – Torralba dataset is proposed. The method is based on K-Means clustering algorithm followed by a novel two phase voting method for classification which is the main contribution of this paper. Two distinct feature sets have been used. The first feature set is used ...

A common pre-processing step is to project the data into a lower-dimensional subspace, before applying k-NN estimator. One example of this is the Eigenfaces algorithm for face recognition. PCA is applied on a database of face images (aligned, of fixed dimension) to get a principal subspace (of much lower dimensionality than the original, which is the number of pixels in the image). For some fix...

A challenging problem of computer vision is scene classification. An efficient method for classifying natural scenes from the Oliva – Torralba dataset is proposed. The method is based on K-Means clustering algorithm followed by a novel two phase voting method for classification which is the main contribution of this paper. Two distinct feature sets have been used. The first feature set is used ...

In this paper we present a method for segmentation of documents image with complex structure. This technique based on GLCM (Grey Level Co-occurrence Matrix) used to segment this type of document in three regions namely, 'graphics', 'background' and 'text'. Very briefly, this method is to divide the document image, in block size chosen after a series of tests and then applying the co-occurrence ...

Being non-invasive and effective at a distance, recognition suffers from low resolution sequence case. In this paper, we attempt to address the issue through the proposed high frequency super resolution method. First, a group of high resolution training gait images are degenerated for capturing high-frequency information loss. Then the combination of neighbor embedding with interpolation method...

This paper presents effective algorithms for reconstructing three-dimensional models of specific curve structures from single perspective view images. The proposed method begins with edge detection and filtering from photographs or paintings with fine perspective geometry. Feature lines and corner points are extracted by transferring detected edges to normal distance and normal angle space. Cur...

We run experiments showing that algorithm clarans (Ng et al., 2005) finds better Kmedoids solutions than the standard algorithm. This finding, along with the similarity between the standard K-medoids and K-means algorithms, suggests that clarans may be an effective K-means initializer. We show that this is the case, with clarans outperforming other popular seeding algorithms on 23/23 datasets w...

The aim of this work is to specify which model of turbulence is the most adapted in order to predict the drag forces that a swimmer encounters during his movement in the fluid environment. For this, a Computational Fluid Dynamics (CFD) analysis has been undertaken with a commercial CFD code (Fluent). The problem was modelled as 3D and in steady hydrodynamic state. The 3D geometry of the swimmer...

An end-member selection method for spectral unmixing that is based on Particle Swarm Optimization (PSO) is developed in this paper. The algorithm uses the K-means clustering algorithm and a method of dynamic selection of end-members subsets to find the appropriate set of end-members for a given set of multispectral images. The proposed algorithm has been successfully applied to test image sets ...

A challenging problem of computer vision is scene classification. An efficient method for classifying natural scenes from the Oliva – Torralba dataset is proposed. The method is based on K-Means clustering algorithm followed by a novel two phase voting method for classification which is the main contribution of this paper. Two distinct feature sets have been used. The first feature set is used ...