The enhanced LBG algorithm

A dichotomy color quantization algorithm for the HSI color space

Color quantization is used to obtain an image with the same number of pixels as the original but represented using fewer colors. Most existing color quantization algorithms are based on the Red Green Blue (RGB) color space, and there are few color quantization algorithms for the Hue Saturation Intensity (HSI) color space with a simple uniform quantization algorithm. In this paper, we propose a dichotomy color quantization algorithm for the HSI color space. The proposed color quantization algorithm can display images with a smaller number of colors than other quantization methods of RGB color space. The proposed algorithm has three main steps as follows: first, a single-valued monotonic function of the Hue (H) component in the from RGB color space to HSI color space (RGB-HSI) color space conversion is constructed, which can avoid the partition calculation of the H component in the RGB-HSI color space; second, an iterative quantization algorithm based on the single-valued monotonic function is proposed; and third, a dichotomy quantization algorithm is proposed to improve the iterative quantization algorithm. Both visual and numerical evaluations reveal that the proposed method presents promising quantization results.

Morphology, Electrical and Optical Properties of Cu Nanostructures Embedded in AZO: A Comparison between Dry and Wet Methods

Herein, Cu nanostructures are obtained by solid-state dewetting of 9 nm copper layer (dry) or by ablating copper target, using a nanosecond pulsed laser at 1064 nm, in acetone and isopropyl alcohol (wet). The Cu nanostructures are embedded in aluminum-doped zinc oxide layer. Then, the electrical, optical, and morphological properties of the two kinds of systems, as a function of their synthesis parameters, are investigated. The aim is to compare the two fabrication methods and select the main conditions to achieve the best system for photovoltaic applications. The main differences, exhibited by the wet and dry processes, were in the shape and size of the Cu nanostructures. Dewetting in nitrogen produces faceted nanoparticles, with an average size below 150 nm, while laser ablation originates spherical and smaller nanoparticles, below 50 nm. Dry system underwent to thermal annealing, which improves the electrical properties, compared to the wet system, with a sheet resistance of 10³ vs. 10⁶ Ω/sq, respectively; finally, the dry system shows a maximum transmittance of 89.7% at 697 nm, compared to the wet system in acetone, 88.4% at 647 nm, as well as in isopropyl alcohol, 86.9% at 686 nm. Moreover, wet systems show higher transmittance in NUV.

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory

ESA defines as Earth Observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006-2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static color names. Typically, a vocabulary of MS color names in a MS data (hyper)cube and a dictionary of land cover (LC) class names in the scene-domain do not coincide and must be harmonized (reconciled). The present Part 1-Theory provides the multidisciplinary background of a priori color naming. The subsequent Part 2-Validation accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data 2006 map, based on an original protocol for wall-to-wall thematic map quality assessment without sampling, where the test and reference maps feature the same spatial resolution and spatial extent, but whose legends differ and must be harmonized.

Genetic Simulated Annealing-Based Kernel Vector Quantization Algorithm

Genetic Algorithm (GA) has been successfully applied to codebook design for vector quantization and its candidate solutions are normally turned by LBG algorithm. In this paper, to solve premature phenomenon and falling into local optimum of GA, a new Genetic Simulated Annealing-based Kernel Vector Quantization (GSAKVQ) is proposed from a different point of view. The simulated annealing (SA) method proposed in this paper can approach the optimal solution faster than the other candidate approaches. In the frame of GA, firstly, a new special crossover operator and a mutation operator are designed for the partition-based code scheme, and then a SA operation is introduced to enlarge the exploration of the proposed algorithm, finally, the Kernel function-based fitness is introduced into GA in order to cluster those datasets with complex distribution. The proposed method has been extensively compared with other algorithms on 17 datasets clustering and four image compression problems. The experimental results show that the algorithm can achieve its superiority in terms of clustering correct rate and peak signal-to-noise ratio (PSNR), and the robustness of algorithm is also very good. In addition, we took “Lena” as an example and added Gaussian noise into the original image then adopted the proposed algorithm to compress the image with noise. Compared to the original image with noise, the reconstructed image is more distinct, and with the parameter value increasing, the value of PSNR decreases.

Statistical analysis of physical-chemical properties and prediction of protein-protein interfaces

We have developed a fully automated method, InterProSurf, to predict interacting amino acid residues on protein surfaces of monomeric 3D structures. Potential interacting residues are predicted based on solvent accessible surface areas, a new scale for interface propensities, and a cluster algorithm to locate surface exposed areas with high interface propensities. Previous studies have shown the importance of hydrophobic residues and specific charge distribution as characteristics for interfaces. Here we show differences in interface and surface regions of all physical chemical properties of residues as represented by five quantitative descriptors. In the current study a set of 72 protein complexes with known 3D structures were analyzed to obtain interface propensities of residues, and to find differences in the distribution of five quantitative descriptors for amino acid residues. We also investigated spatial pair correlations of solvent accessible residues in interface and surface areas, and compared log-odds ratios for interface and surface areas. A new scoring method to predict potential functional sites on the protein surface was developed and tested for a new dataset of 21 protein complexes, which were not included in the original training dataset. Empirically we found that the algorithm achieves a good balance in the accuracy of precision and sensitivity by selecting the top eight highest scoring clusters as interface regions. The performance of the method is illustrated for a dimeric ATPase of the hyperthermophile, Methanococcus jannaschii, and the capsid protein of Human Hepatitis B virus. An automated version of the method can be accessed from our web server at http://curie.utmb.edu/prosurf.html.

Partial distortion entropy maximization for online data clustering

Competitive learning neural networks are regarded as a powerful tool for online data clustering to represent a non-stationary probability distribution with a fixed number of weight vectors. One difficulty in practical applications of competitive learning neural networks to online data clustering is that most of them require heuristically-predetermined threshold parameters for balancing a trade-off between convergence accuracy, i.e. error minimization performance, and speed of adaptation to the changes in source statistics. Although adaptation acceleration is achievable by relocating a "useless" node so that it becomes useful, excessive relocation often disturbs error minimization. Hence, both of the adaptation speed and the error minimization performance sensitively depend on threshold parameters to determine whether a node should be relocated or not. In general, it is difficult to know adequate threshold parameters a priori. This paper proposes a novel criterion for decision making of node relocation without heuristically predetermined thresholds. According to the proposed criterion, a node is relocated only if the relocation task improves partial distortion entropy, which is an online optimality metric reliable from the viewpoint of error minimization. Hence, node relocation is carried out without disturbing error minimization. As a result, both quick adaptation and error minimization are simultaneously accomplished without any carefully predefined parameters. Experimental results clarify the validity of the proposed criterion. Competitive learning with the criterion is clearly superior to other representative algorithms in terms of both quick adaptation and error minimization performance.

A multiscale expectation-maximization semisupervised classifier suitable for badly posed image classification

This paper deals with the problem of badly posed image classification. Although underestimated in practice, bad-posedness is likely to affect many real-world image classification tasks, where reference samples are difficult to collect (e.g., in remote sensing (RS) image mapping) and/or spatial autocorrelation is relevant. In an image classification context affected by a lack of reference samples, an original inductive learning multiscale image classifier, termed multiscale semisupervised expectation maximization (MSEM), is proposed. The rationale behind MSEM is to combine useful complementary properties of two alternative data mapping procedures recently published outside of image processing literature, namely, the multiscale modified Pappas adaptive clustering (MPAC) algorithm and the sample-based semisupervised expectation maximization (SEM) classifier. To demonstrate its potential utility, MSEM is compared against nonstandard classifiers, such as MPAC, SEM and the single-scale contextual SEM (CSEM) classifier, besides against well-known standard classifiers in two RS image classification problems featuring few reference samples and modestly useful texture information. These experiments yield weak (subjective) but numerous quantitative map quality indexes that are consistent with both theoretical considerations and qualitative evaluations by expert photointerpreters. According to these quantitative results, MSEM is competitive in terms of overall image mapping performance at the cost of a computational overhead three to six times superior to that of its most interesting rival, SEM. More in general, our experiments confirm that, even if they rely on heavy class-conditional normal distribution assumptions that may not be true in many real-world problems (e.g., in highly textured images), semisupervised classifiers based on the iterative expectation maximization Gaussian mixture model solution can be very powerful in practice when: 1) there is a lack of reference samples with respect to the problem/model complexity and 2) texture information is considered negligible (i.e., a piecewise constant image model holds).

An adaptive incremental LBG for vector quantization

This study presents a new vector quantization method that generates codewords incrementally. New codewords are inserted in regions of the input vector space where the distortion error is highest until the desired number of codewords (or a distortion error threshold) is achieved. Adoption of the adaptive distance function greatly increases the proposed method's performance. During the incremental process, a removal-insertion technique is used to fine-tune the codebook to make the proposed method independent of initial conditions. The proposed method works better than some recently published efficient algorithms such as Enhanced LBG (Patane, & Russo, 2001) for traditional tasks: with fixed number of codewords, to find a suitable codebook to minimize distortion error. The proposed method can also be used for new tasks that are insoluble using traditional methods: with fixed distortion error, to minimize the number of codewords and find a suitable codebook. Experiments for some image compression problems indicate that the proposed method works well.

Determining functionally important amino acid residues of the E1 protein of Venezuelan equine encephalitis virus

A new method for predicting interacting residues in protein complexes, InterProSurf, was applied to the E1 envelope protein of Venezuelan equine encephalitis (VEEV). Monomeric and trimeric models of VEEV-E1 were constructed with our MPACK program, using the crystal structure of the E1 protein of Semliki forest virus as a template. An alignment of the E1 sequences from representative alphavirus sequences was used to determine physical chemical property motifs (likely functional areas) with our PCPMer program. Information on residue variability, propensity to be in protein interfaces, and surface exposure on the model was combined to predict surface clusters likely to interact with other viral or cellular proteins. Mutagenesis of these clusters indicated that the predictions accurately detected areas crucial for virus infection. In addition to the fusion peptide area in domain 2, at least two other surface areas play an important role in virus infection. We propose that these may be sites of interaction between the E1-E1 and E1-E2 subdomains of the envelope proteins that are required to assemble the functional unit. The InterProSurf method is, thus, an important new tool for predicting viral protein interactions. These results can aid in the design of new vaccines against alphaviruses and other viruses.

An incremental network for on-line unsupervised classification and topology learning

This paper presents an on-line unsupervised learning mechanism for unlabeled data that are polluted by noise. Using a similarity threshold-based and a local error-based insertion criterion, the system is able to grow incrementally and to accommodate input patterns of on-line non-stationary data distribution. A definition of a utility parameter, the error-radius, allows this system to learn the number of nodes needed to solve a task. The use of a new technique for removing nodes in low probability density regions can separate clusters with low-density overlaps and dynamically eliminate noise in the input data. The design of two-layer neural network enables this system to represent the topological structure of unsupervised on-line data, report the reasonable number of clusters, and give typical prototype patterns of every cluster without prior conditions such as a suitable number of nodes or a good initial codebook.

Survey of clustering algorithms

Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.