A Novel Approach Using PCA and SVM for Face Detection
Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on principal components analysis (PCA) and support vector machine (SVM) is proposed. It firsly filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, SVM classifier is used to detect face feature in the test image, SVM has great performance in classification task. PCA is used to reduce dimension of sample data. After PCA transform, the feature vectors, which are used for training SVM classifier, are generated. Our tests in this paper are based on CMU face database. The experimental results demonstrate that the proposed method is encouraging with a successful detection rate.

title: Texture-Based Image Retrieval by Edge Detection Matching GLCM
abstract: Content-Based Image Retrieval (CBIR) system is emerging as an important research area, users can search and retrieve images based on their properties such as shape, color and texture from the image database. Usually texture-based image retrieval just consider an original image of coarseness, contrast and roughness, actually there is much texture information in the edge image. This paper proposed a novel approach to retrieve images by texture characterization using a composition of edge information and co-occurrence matrix properties. The proposed method gives encouraging results when comparing its retrieval performance to that of the traditional co-occurrence matrices and Yaopsilas approach.

title: Leopard Image Retrieval using Region of Interest and Texture Feature
abstract: In content based image retrieval system, texture based natural image retrieval can not extract texture feature efficiently and accurately, because the nature image is very complex and most of method extract feature use the whole nature image. In this paper, a new method is proposed for leopard texture image retrieval. Usually, the habitat of leopard is grass or tree, there is large difference between leopard image background and foreground, so it is easy to separate the leopard region from background using Canny edge detection, and then extract region of interest of foreground. The similarity of two images will be determined by the similarities between pairs of region of interest. The experimental results on the leopard texture image database demonstrate that the proposed method is encouraging with an average successful retrieval rate of 95.66%

title: ROI Based Natural Image Retrieval Using Color and Texture Feature
abstract: Region of interest(ROI) plays an important role in image analysis. In this paper, an efficient approach for content based image retrieval combining both color and texture features using three ROIs is proposed. Firstly, segment image to three parts using K-means algorithm. Secondly, select three ROIs from the three parts and then extract color features and texture features of ROIs. The similarity of two images will be determined by the similarities between pairs of ROIs. The experimental results demonstrate that the proposed method is encouraging with a successful retrieval rate.

title: A Novel Approach Using Edge Detection Information for Texture Based Image Retrieval
abstract: Most texture-based image retrieval system just consider an original image of coarseness, contrast and roughness, actually there are many texture information in the edge image. In this paper, a method combining both edge information and gray level co-occurrence matrix properties is proposed to improve the retrieval performance. The proposed method gives encouraging results when comparing its retrieval performance to that of the Yao's method, in the same image database.

title: Landmine Feature Extraction and Classification of GPR Data Based on SVM Method
abstract: In this paper, the problem of detecting buried landmine is tackled in the feature extraction and classification. Determining the likelihood set of an unknown pattern (feature vector), extracted from ground penetrating radar data by using SVM method. The advantage of SVM method in feature extraction and classification of image processing is: A classifier works well both on the training samples and on previously unseen samples; In addition, the SVM provides, enable a classification performance improvement based on from high feature dimensions to two or three feature dimensions. Finally, SVM method has a standard theory and a good implementation algorithm.

title: An Improved Fuzzy Image Enhancement Algorithm
abstract: An effective method for fuzzy image enhancement was presented by Russo, which was controlled by tuning of one parameter. The fixed parameter was used for the entire image resulting in over blurring or sharpening of image features in some parts of the image. On the basis of these, in this paper, we applied Russo's algorithm on different kinds of noise and proposed an efficient method to obtain the parameter values adaptively. Firstly, each pixel went through a phase of smoothing and then followed by a phase of combining smoothing and sharpening; Secondly, to effectively enhance image, each pixel local feature was adaptively assigned different parameter values by evaluating pixel local features through a fuzzy membership function; Finally, compared with Russopsilas method and other methods, experimental results indicated that the proposed method could achieve better performance than traditional methods for the enhancement of images corrupted with impulse and Gaussian noise.

title: A Pixel-level Statistical Structural Descriptor for Shape Measure and Recognition
abstract: A novel shape descriptor based on the histogram matrix of pixel-level structural features is presented. First, length ratios and angles between the centroid and contour points of a shape are calculated as two structural attributes. Then, the attributes are combined to construct a new histogram matrix in the feature space statistically. The proposed shape descriptor can measure circularity, smoothness, and symmetry of shapes, and be used to recognize shapes. Experimental results demonstrate the effectiveness of our method.

title: A Simplified Learning Algorithm of Incremental Bayesian
abstract: A proportion factor is constructed though the Maximum Aposteriori Probability of examples in test data to select the training examples in incremental learning process. Instead of complex normal classify loss expression, the proportion factor λ is used to estimate the classify loss to improve classification efficiency. The final experiment shows that this algorithm is feasible, and more accurate than simple Bayesian classifier. The computing time is highly reduced on the optimal selection of examples in incremental learning process.

title: dbNEI2.0: building multilayer network for drug-NEI-disease
abstract: The neuro-endocrine-immune (NEI) system plays a critical regulatory role in modulating host homeostasis and optimizing health. We created the dbNEI 2 years ago to collect NEI molecules and interactions. For transferring the conceptual NEI to the systematic NEI network and uncovering the NEI's medical function, we updated the dbNEI 2.0 in three ways: (i) extended NEI molecules to 2242 genes and 7657 chemical compounds by using gene ontology-based (GO-based) data mining strategy, (ii) added multilayer interactions of NEI molecules including KEGG signal transduction and metabolic pathways, HPRD protein-protein interactions (PPI), transcription factor and microRNA regulations and (iii) connected 611 drugs and 823 diseases through multilayer NEI interactions. The reconstructed drug-NEI-disease network will facilitate the systematic study of NEI system.