26794680
Texture segmentation based on MRMRF modeling
Texture segmentation remains a fundamental issue in low-level image analysis, pattern recognition and computer vision. Texture segmentation problem can be solved in two directions: model fitting and non-parametric classification. In this paper, we propose to use multiresolution MRF (MRMRF) modeling in texture segmentation. A novel MRMRF parameter estimation method based on MCMC approach is presented. The experimental result shows that the method is suitable to segment textured images.
5903999
Texture classification using multiresolution Markov random field models
Abstract Texture classification is an important topic in texture analysis. Texture classification has wide applications in remote sensing, computer vision, and image analysis. During the past years, several authors discussed to use multiresolution stochastic approaches to model textures. However, in these approaches, the highpass components which contain rich detailed information are lost. In this paper, we propose multiresolution MRF (MRMRF) modeling to describe textures. MRMRF modeling is a method trying to fuse filtering theory and MRF models. In the MRMRF modeling, highpass components are considered as well as lowpass components. “Brodatz texture database” is used in this paper for the experiments and Nearest Linear Combination (NLC) is used as measurement of distance to improve the recognition rate. The experimental results show that NLC has much better performance than Nearest Neighbor (NN) as the measurement in MRMRF modeling.
32712174
Hailstone Detection Based on Image Mining
Hailstone is one of the main meteorological disasters and it is very difficult to be forecasted effectively. A novel method of automatic recognition hail cloud is proposed base on association rules in this paper. Firstly, the method preprocesses the original reflectivity image. Then the color features and texture features are extracted from the radar echo image. Lastly, the association rules, which are used to distinguish hailstorm from rainstorm and super refract, are constructed, namely hail echo ensemble forecast model based on image mining is constructed. The results show that hail echo ensemble forecast model based on image mining has higher accuracy compared with the PUP products.
23948133
Building Domain Ontology Based on Web Data and Generic Ontology
The automatic or semi-automatic construction of ontology has become a research topic of interest in recent years. This paper describes a mechanism for constructing domain specific ontologies automatically based on web data and generic ontology. Firstly, we employ the hierarchical agglomerative clustering(HAC) algorithm, clustering web pages hierarchically and resulting in a binary tree. Then an algorithm is proposed, which selectd from the binary tree the significative nodes as topics implying concepts of domain interests. Lastly, the Chinese generic ontology, HowNet, is introduced to evolve the topics (together with their hierarchical structures) into domain ontology. We experiment our method in the field of computer hardware based on web pages collected from Chinese BtoC web sites. An in-depth discussion on the experiment results is also given.
38163613
Bayesian Learning for Image Retrieval Using Multiple Features
Image retrieval using multiple features often uses explicit weights that represent the importance of the features in their similarity metrics. In this paper, a novel retrieval method based on Bayesian Learning is presented. Instead of giving every feature a weight explicitly, the importance of a feature is regulated implicitly by learning a user's perception. Thus, the process of feature combination is adaptive and approximate to a user's perception. Experimental results demonstrate the significance of this method for improving the retrieval efficiency.
12805970
A Kernel-Induced Space Selection Approach to Model Selection in KLDA
<para> Model selection in kernel linear discriminant analysis (KLDA) refers to the selection of appropriate parameters of a kernel function and the regularizer. By following the principle of <emphasis emphasistype="bold"><emphasis emphasistype="italic">maximum information preservation</emphasis></emphasis>, this paper formulates the model selection problem as a problem of selecting an optimal kernel-induced space in which different classes are maximally separated from each other. A scatter-matrix-based criterion is developed to measure the “goodness” of a kernel-induced space, and the kernel parameters are tuned by maximizing this criterion. This criterion is computationally efficient and is differentiable with respect to the kernel parameters. Compared with the leave-one-out (LOO) or <formula formulatype="inline"><tex Notation="TeX">$k$</tex></formula>-fold cross validation (CV), the proposed approach can achieve a faster model selection, especially when the number of training samples is large or when many kernel parameters need to be tuned. To tune the regularization parameter in the KLDA, our criterion is used together with the method proposed by Saadi <etal/> (2004). Experiments on benchmark data sets verify the effectiveness of this model selection approach. </para>
27385183
Standard Deviation and Intra Prediction Mode Based Adaptive Spatial Error Concealment (SEC) in H.264/AVC
Transmission of compressed video over error prone channels may result in packet losses or errors, which can significantly degrade the image quality. Therefore an error concealment scheme is applied at the video receiver side to mask the damaged video. Considering there are 3 types of MBs (Macro Blocks) in natural video frame, i.e., Textural MB, Edged MB, and Smooth MB, this paper proposes an adaptive spatial error concealment which can choose 3 different methods for these 3 different MBs. For criteria of choosing appropriate method, 2 factors are taken into consideration. Firstly, standard deviation of our proposed edge statistical model is exploited. Secondly, some new features of latest video compression standard H.264/AVC, i.e., intra prediction mode is also considered for criterion formulation. Compared with previous works, which are only based on deterministic measurement, proposed method achieves the best image recovery. Subjective and objective image quality evaluations in experiments confirmed this.