Global Structural Features Research Articles

Accurate Measurement of Residual Dipolar Couplings in Large RNAs by Variable Flip Angle NMR.

NMR approaches using nucleotide-specific deuterium labeling schemes have enabled structural studies of biologically relevant RNAs of increasing size and complexity. Although local structure is well-determined using these methods, definition of global structural features, including relative orientations of independent helices, remains a challenge. Residual dipolar couplings, a potential source of orientation information, have not been obtainable for large RNAs due to poor sensitivity resulting from rapid heteronuclear signal decay. Here we report a novel multiple quantum NMR method for RDC determination that employs flip angle variation rather than a coupling evolution period. The accuracy of the method and its utility for establishing interhelical orientations are demonstrated for a 36-nucleotide RNA, for which comparative data could be obtained. Applied to a 78 kDa Rev response element from the HIV-1 virus, which has an effective rotational correlation time of ca. 160 ns, the method yields sensitivity gains of an order of magnitude or greater over existing approaches. Solution-state access to structural organization in RNAs of at least 230 nucleotides is now possible.

Simultaneous refinement of inaccurate local regions and overall structure in the CASP12 protein model refinement experiment.

Advances in protein model refinement techniques are required as diverse sources of protein structure information are available from low-resolution experiments or informatics-based computations such as cryo-EM, NMR, homology models, or predicted residue contacts. Given semi-reliable or incomplete structural information, structure quality of a protein model has to be improved by ab initio methods such as energy-based simulation. In this study, we describe a new automatic refinement server method designed to improve locally inaccurate regions and overall structure simultaneously. Locally inaccurate regions may occur in protein structures due to non-convergent or missing information in template structures used in homology modeling or due to intrinsic structural flexibilities not resolved by experimental techniques. However, such variable or dynamic regions often play important functional roles by participating in interactions with other biomolecules or in transitions between different functional states. The new refinement method introduced here utilizes diverse types of geometric operators which drive both local and global changes, and the effect of structure changes and relaxations are accumulated. This resulted in consistent refinement of both local and global structural features. Performance of this method in CASP12 is discussed.

Limits of local algorithms over sparse random graphs

Local algorithms on graphs are algorithms that run in parallel on the nodes of a graph to compute some global structural feature of the graph. Such algorithms use only local information available at nodes to determine local aspects of the global structure, while also potentially using some randomness. Recent research has shown that such algorithms show significant promise in computing structures like large independent sets in graphs locally. Indeed the promise led to a conjecture by Hatami, Lovász and Szegedy [Geom. Funct. Anal. 24 (2014) 269–296] that local algorithms defined specifically as so-called i.i.d. factors may be able to find approximately largest independent sets in random $d$-regular graphs. In this paper, we refute this conjecture and show that every independent set produced by local algorithms is multiplicative factor $1/2+1/(2\sqrt{2})$ smaller than the largest, asymptotically as $d\rightarrow\infty$. Our result is based on an important clustering phenomena predicted first in the literature on spin glasses, and recently proved rigorously for a variety of constraint satisfaction problems on random graphs. Such properties suggest that the geometry of the solution space can be quite intricate. The specific clustering property that we prove and apply in this paper shows that typically every two large independent sets in a random graph either have a significant intersection, or have a very small intersection. As a result, large independent sets are clustered according to the proximity to each other. While the clustering property was postulated earlier as an obstruction for the success of local algorithms, our result is the first one where the clustering property is used to formally prove limits on local algorithms.

Interfacial free energy and medium range order: Proof of an inverse of Frank's hypothesis

We study the relation of crystal-liquid interfacial free energy and medium range order in the quasicrystal-forming $\mathrm{T}{\mathrm{i}}_{37}\mathrm{Z}{\mathrm{r}}_{42}\mathrm{N}{\mathrm{i}}_{21}$ liquid from undercooling experiment and ab initio molecular dynamics (MD) simulation. Adding a small amount of Ag to the liquid significantly reduces the degree of undercooling, which is suggestive of small interfacial free energy, and thus very similar atomic configuration between the liquid and the icosahedral quasicrystal phases. Using ab initio MD study, we find that Ag atoms predominantly form a bond with Zr atoms in the short range and, further, Ag-Zr pairs are extended in the liquid, as a medium range order which is identical to the global structural feature reported recently [Liu et al., Phys. Rev. Lett. 105, 155501 (2010)]. This result may expect extremely small undercooling if the icosahedral medium range order exists in a liquid forming an icosahedral quasicrystal, which implies the ambiguity of clear distinction of heterogeneous and homogeneous nucleation.

The Transcriptional Network Structure of a Myeloid Cell: A Computational Approach.

Understanding the general principles underlying genetic regulation in eukaryotes is an incomplete and challenging endeavor. The lack of experimental information regarding the regulation of the whole set of transcription factors and their targets in different cell types is one of the main reasons to this incompleteness. So far, there is a small set of curated known interactions between transcription factors and their downstream genes. Here, we built a transcription factor network for human monocytic THP-1 myeloid cells based on the experimentally curated FANTOM4 database where nodes are genes and the experimental interactions correspond to links. We present the topological parameters which define the network as well as some global structural features and introduce a relative inuence parameter to quantify the relevance of a transcription factor in the context of induction of a phenotype. Genes like ZHX2, ADNP, or SMAD6 seem to be highly regulated to avoid an avalanche transcription event. We compare these results with those of RegulonDB, a highly curated transcriptional network for the prokaryotic organism E. coli, finding similarities between general hallmarks on both transcriptional programs. We believe that an approach, such as the one shown here, could help to understand the one regulation of transcription in eukaryotic cells.

Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions

The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a ‘scoring fusion’ artificial neural network classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC = 0.793 ± 0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions.

MGNPE‐LICA algorithm for fault diagnosis of batch process

AbstractBatch production process data usually contains mixture ingredients of the Gaussian and non‐Gaussian distribution. Traditional monitoring methods require data to meet the requirements of Gaussian distribution and do not take into account the global and local features for feature extraction. An ICA algorithm can deal with process diagnosis of non‐Gaussian distribution but cannot deal with process diagnosis of Gaussian distribution. So a multi‐way global neighbourhood preserving embedding, local independent component analysis (MGNPE‐LICA) algorithm, is proposed in this paper. Firstly, raw data is divided into Gaussian and non‐Gaussian spaces by the D‐test. For the Gaussian space, the MGNPE algorithm is used to fully extract local structural features and global structural features of data. For the non‐Gaussian space, the MLICA algorithm is used to solve non‐Gaussian problems and at the same time reserve global and local information of data. Then the monitoring index of two spaces synthesizes a joint monitoring indicator to monitor the process. The contribution plot method is used to diagnose fault variables after detecting faults. The simulation results of a penicillin fermentation process verified the effectiveness of the proposed algorithm.

Tensor LRR and Sparse Coding-Based Subspace Clustering.

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established stateof- the-art methods.

Typology of countries of the world according to the development level of international tourism

A multifactor tourism typology of countries of the world having a structural-functional character has been developed. It provides insight into the typological features of the global spatial structure of international tourism, reflecting the intensity of international tourism flows, their geography, the importance of the tourism industry for the national economy as well as the general attributes of territorial organization of international tourism, and the dynamics of its development. Five main types of touristic types of countries have been identified. The following indicators were used for characterization of the countries and their comparison: the size of the territory, and the population size; the general level of socioeconomic development; the level and character of development of international tourism; the characteristic properties of touristic demand; the orientedness of tourism flows; characteristics of touristic offer; the degree of influence of international tourism on the national economy; characteristics of the state policy in the sphere of tourism, and touristic specialization. The first type includes the economically highly developed countries with mature national systems of international tourism forming the center of the world’s touristic space. The second type is represented by countries belonging to the semi-periphery of the world’s touristic space with an intensely forming state-supported polyfunctional system of international tourism. The third type includes moderately developed countries of the semi-periphery of the world’s touristic space with a polyfunctional system of international tourism in the stage of transformation and formation of a civilized travel market. The fourth type comprises small island states of the Caribbean Basin which belong to the semi-periphery and, in part, to the “advanced” periphery of the world’s touristic space. The fifth type combines developing countries belonging to the periphery of the world’s touristic space, with a weakly developed market of international tourism.

Enhancer Landscapes Reveal Transcription Factor Network Dependencies in Chronic Lymphocytic Leukemia

Genome sequencing efforts of chronic lymphocytic leukemia have revealed mutations that disrupt protein-coding elements of the genome (Puente et al, 2011; Wang et al, 2011; Landau et al, 2013). Recently, comprehensive whole-genome sequencing efforts have begun to reveal the genetic aberrations that occur outside of protein-coding exons, many that may perturb gene regulatory sites (Puente et al, 2015). These include enhancer elements that make physical contact with gene promoters to regulate gene expression in a cell-type specific manner. While mutations certainly promote CLL leukemogenesis, epigenomic alterations may also play an important role in facilitating disease progression and maintenance by inducing the gene expression aberrations that have long been observed in CLL. Epigenomic alterations include chromatin structure changes that facilitate altered transcription and chromatin factor recruitment to regulatory elements. While comprehensive genome-wide DNA methylation studies have been performed on human cancers and normal cell counterparts including CLL, other comprehensive studies of cancer epigenomes have been lacking. We have completed an analysis of chromatin structures in a cohort of primary chronic lymphocytic leukemia (CLL) samples with comparisons to normal CD19+ B lymphocytes (n = 18 CLL samples, n = 5 normal B lymphocyte samples). We used chromatin accessibility assays (ATAC-seq) and genome-wide enhancer mapping (H3K27ac ChIP-seq) to comprehensively define the transcriptionally active chromatin landscape of CLL. We have discovered greater than 15,000 novel regulatory elements when compared to previously annotated regulatory elements. Moreover, sites within the loci of several hundred genes were found to have large regions of gained chromatin accessibility and H3K27 acetylation, revealing the appearance of aberrant enhancer activity. These gained enhancer elements correspond with increased gene expression and are found at gene loci such as LEF1, PLCG1, CTLA4, and ITGB1. We have also systematically identified the super-enhancers of CLL - large complex regulatory regions that possess unique tissue-specific regulatory capabilities. Many of these super-enhancers are found in normal B lymphocytes, yet the super-enhancer at the ITGB1 and LEF1 loci are CLL-specific and may be considered to facilitate leukemia-specific expression. We have found CLL-specific enhancers are also significantly associated with annotated CLL risk variants, and have identified enhancer-associated SNPs found within CLL-risk loci predicted to disrupt transcription factor binding sites. These include SNPs at the IRF8 and LEF1 locithat lead to the creation and destruction of SMAD4 and RXRA binding sites, respectively. Additionally, we have analyzed whole-genome sequencing data from a subset of our sample cohort. Mutational hotspots in the CXCR4 and BACH2 promoters occur within open, acetylated regions. Moreover, we discover recurrent mutations in enhancers of the ETS1 and ST6GAL1 locus that have not been previously annotated. Using a transcription factor network modeling approach, we used these global chromatin structure characteristics to determine networks that are highly active in CLL. We find that transcription factors such as NFATc1, E2F5, and NR3C2 are among the most interconnected transcription factors of the CLL genome, and their connectivity is significantly higher in CLL cells compared to normal B cells. In contrast, network profiling of CLL cells predicts loss of MXI1 connectivity, a negative regulator of the MYC oncogene. By treating cells with specific pharmacological inhibitors of NFAT family members including cyclosporin and FK506, we are able to reduce NFAT-mediated network connectivity, resulting in a selective loss of NFAT-bound enhancers. This leads to CLL cell death in vitro of both cell lines and primary CLL patient samples. Our results reveal the unique chromatin structure landscape of CLL for the first time, and identify the CLL-specific enhancer elements that confer the transcriptional dysregulation that has long been observed in this disease. Use of these chromatin structure analyses and enhancer landscapes has allowed us to construct the intrinsic transcription factor network of CLL, and determine a particular dependency on NFAT signaling for cell survival. DisclosuresNo relevant conflicts of interest to declare.

Status of Aquarius/SAC-D and Aquarius Salinity Retrievals

Aquarius was launched in June 2011 to monitor the global salinity field in the open ocean. This radiometer/scatterometer (i.e., passive/active) instrument is part of the Aquarius/SAC-D mission, a partnership between the USA and Argentina. In general, the observatory and instruments have functioned well with mostly minor issues. Aquarius has been operating successfully since being turned on August 25, 2011. The quality of the salinity retrieval has improved continuously and is approaching the accuracy goal of 0.2 psu (monthly global RMS). The maps produced by Aquarius show the global structure and dynamic features of the salinity field and, now after 3 years of operations, data are available to give a first look at the interannual changes in sea surface salinity. An improved salinity product, Version 3.0, has been released to the public (June 2014) and includes significant refinements in the retrieval algorithm such as reduced ascending/descending differences and improved calibration. Additional data such as global maps of soil moisture and maps of RFI are also available.

Compression approach of street networks considering the structural and functional features of streets

The compression of networks is an important aspect of complex networks and spatial generalization. Previous studies show that the dual graph for street–street relationships more accurately reflects the morphological features of street networks than the traditional methods. In this study, a dual graph for street–street relationship is constructed based on complex networks theory. We introduce the concept of m-order neighbors and take into account the factors of the node’s degree, closeness centrality, betweenness centrality, and distance within the dual graph. We also consider the importance contributions of the node itself and its 1- to m-order neighbors and define the evaluation model of node importance. We then propose a street compression process based on the evaluation of node importance for dual graph by considering the structural and functional features of streets. The degree distribution and topological similarity index are introduced to evaluate the level of maintaining the global structure and topological characteristics of the road network and to validate the efficiency of the proposed method. A real urban road network is used for the experiments. Results show that the proposed approach can be used in selecting important streets that can retain the global structural properties and topological connectivity of the street network.

Object tracking using discriminative sparse appearance model

Object tracking based on sparse representation formulates tracking as searching the candidate with minimal reconstruction error in target template subspace. The key problem lies in modeling the target robustly to vary appearances. The appearance model in most sparsity-based trackers has two main problems. The first is that global structural information and local features are insufficiently combined because the appearance is modeled separately by holistic and local sparse representations. The second problem is that the discriminative information between the target and the background is not fully utilized because the background is rarely considered in modeling. In this study, we develop a robust visual tracking algorithm by modeling the target as a model for discriminative sparse appearance. A discriminative dictionary is trained from the local target patches and the background. The patches display the local features while their position distribution implies the global structure of the target. Thus, the learned dictionary can fully represent the target. The incorporation of the background into dictionary learning also enhances its discriminative capability. Upon modeling the target as a sparse coding histogram based on this learned dictionary, our tracker is embedded into a Bayesian state inference framework to locate a target. We also present a model update scheme in which the update rate is adjusted automatically. In conjunction with the update strategy, the proposed tracker can handle occlusion and alleviate drifting. Comparative results on challenging benchmark image sequences show that the tracking method performs favorably against several state-of-the-art algorithms.

Multi-scale mesh saliency based on low-rank and sparse analysis in shape feature space

This paper advocates a novel multi-scale mesh saliency method using the powerful low-rank and sparse analysis in shape feature space. The technical core of our approach is a new shape descriptor that embraces both local geometry information and global structure information in an integrated way. Our shape descriptor is organized in a layered and nested structure, enabling both multi-scale and multi-level functionalities. Upon devising our novel shape descriptor, the remaining challenge is to accurately capture sub-region (or sub-part) saliency from 3D geometric models. Towards this goal, we exploit our novel shape descriptor to define local-to-global shape context in a vertex-wise fashion and concatenate all the shape contexts to form a feature space, which encodes both local geometry feature and global structure feature. It then paves the way for us to employ the powerful low-rank and sparse analysis in the feature space, because the low-rank components emphasize much more on stronger patch/part similarities, and the sparse components correspond to their differences. By focusing on the sparse components, we develop a versatile, structure-sensitive saliency detection framework, which can distinguish local geometry saliency and global structure saliency in various 3D geometric models. Our extensive experiments have exhibited many attractive properties of our novel shape descriptor, including: being suitable for perception-driven analysis, being structure-sensitive, multi-scale, discriminative, and effectively capturing the intrinsic characteristic of the underlying geometry.

Thermal Stabilization of Adenylate Kinases by Optimizing Local Structural Entropy

Local structural entropy (LSE) is an empirical descriptor for the extent of conformational variability in short protein sequences, computed from structural information derived from structures in the Protein Data Bank. Reducing the LSE of a protein sequence by mutating amino acid residues can result in fewer conformational states and thus a more stable structure. Here, we report a series of LSE optimization experiments designed to stabilize mesophilic and thermophilic adenylate kinases (AKs). The crystal structures of the LSE-optimized AK variants reveal that stabilization by LSE reduction may result from the optimization of local hydrophobic contacts. The limitations of the LSE optimization are likely due to ignorance of interactions connecting distant regions of the polypeptide. Our results illustrate the effectiveness and limitations of LSE optimization as a protein stabilization method and highlight the complementarity of local and global structural features in protein thermal stability.

An incremental structured part model for object recognition

In this paper, we describe how to build an incremental structured part model for object recognition. The proposed method explores both global structural information and multiple local features of objects for object model characterization. It use part models to represent structure nodes, which encode the local information of an object. The parts are learned through a segmentation and clustering process, and are used to form the part models in terms of multiple feature fusion and multi-class SVMs. The structured part model is then constructed by correlating different parts through a deformable configuration. Furthermore, we present an incremental learning strategy, which learns a part model by using only a small number of training samples. Annotated images with high entropies are used to update the trained model. The advantage of our method is that it captures the inherent connections of the semantic parts of objects and characterizes the structural relationships between them. The proposed approach is evaluated on two datasets and demonstrates advantages over several state-of-the-art part-based methods in the literature.

Replica-exchange molecular dynamics simulations of cellulose solvated in water and in the ionic liquid 1-butyl-3-methylimidazolium chloride.

Ionic liquids have become a popular solvent for cellulose pretreatment in biorefineries due to their efficiency in dissolution and their reusability. Understanding the interactions between cations, anions, and cellulose is key to the development of better solvents and the improvement of pretreatment conditions. While previous studies described the interactions between ionic liquids and cellulose fibers, shedding light on the initial stages of the cellulose dissolution process, we study the end state of that process by exploring the structure and dynamics of a single cellulose decamer solvated in 1-butyl-3-methyl-imidazolium chloride (BmimCl) and in water using replica-exchange molecular dynamics. In both solvents, global structural features of the cellulose chain are similar. However, analyses of local structural properties show that cellulose explores greater conformational variability in the ionic liquid than in water. For instance, in BmimCl the cellulose intramolecular hydrogen bond O3H'···O5 is disrupted more often resulting in greater flexibility of the solute. Our results indicate that the cellulose chain is more dynamic in BmimCl than in water, which may play a role in the favorable dissolution of cellulose in the ionic liquid. Calculation of the configurational entropy of the cellulose decamer confirms its higher conformational flexibility in BmimCl than in water at elevated temperatures.

Cartographic generalization of urban street networks based on gravitational field theory

The automatic generalization of urban street networks is a constant and important aspect of geographical information science. Previous studies show that the dual graph for street–street relationships more accurately reflects the overall morphological properties and importance of streets than do other methods. In this study, we construct a dual graph to represent street–street relationship and propose an approach to generalize street networks based on gravitational field theory. We retain the global structural properties and topological connectivity of an original street network and borrow from gravitational field theory to define the gravitational force between nodes. The concept of multi-order neighbors is introduced and the gravitational force is taken as the measure of the importance contribution between nodes. The importance of a node is defined as the result of the interaction between a given node and its multi-order neighbors. Degree distribution is used to evaluate the level of maintaining the global structure and topological characteristics of a street network and to illustrate the efficiency of the suggested method. Experimental results indicate that the proposed approach can be used in generalizing street networks and retaining their density characteristics, connectivity and global structure.

An Adaptive and Integrated Multimodal Sensing And Processing Framework For Long-Range Moving Object Detection And Classification

In applications such as surveillance, inspection and traffic monitoring, long-range detection and classification of targets (vehicles, humans, etc) is a highly desired feature for a sensing system. A single modality will no longer provide the required performance due to the challenges in detection and classification with low resolutions, noisy sensor signals, and various environmental factors due to large sensing distances. Multimodal sensing and processing, on the other hand, can provide complementary information from heterogeneous sensor modalities, such as audio, visual and range sensors. However, there is a lack of effective sensing mechanisms and systematic approaches for sensing and processing using multimodalities. In this thesis, a systematical framework is proposed for Adaptive and Integrated Multimodal Sensing and Processing (AIM-SP) that integrates novel multimodal long-range sensors, adaptive feature selection and learning-based object detection and classification for achieving the goal of adaptive and integrated multimodal sensing and processing. Based on the AIM-SP framework, we have made three unique contributions. First, we have designed a novel multimodal sensor system called Vision-Aided Automated Vibrometry (VAAV), consists of a laser Doppler vibrometer (LDV) and a pair of pan-tilt-zoom (PTZ) cameras, and the system is capable of automatically obtaining visual, range and acoustic signatures for moving object detection at a large distance. It provides a close loop adaptive sensing that allows determination of good surface points and quickly focusing the laser beam of the LDV based on the target detection, surface selection, and distance measurements by the PTZ pair and acoustic signal feedbacks of the LDV. Second, multimodal data of vehicles on both local roads and highways, acquired from multiple sensing sources, are integrated and represented in a Multimodal Temporal Panorama (MTP) for easy alignment and fast labelling of the multimodal data: visual, audio and range. Accuracy of target detection can be improved using multimodalities, and a visual reconstruction method is developed to remove occlusions, motion blurs and perspective distortions of moving vehicles so that scale- and perspective-invariant visual vehicle features are obtained. The concept of MTP is not limited to visual and audio information, but is also applicable when other modalities are available that can be presented in the same time axis. With various types of features extracted on aligned multimodal samples, we made our third contribution on feature modality selection using two approaches. The first approach uses multi-branch sequential-based feature searching (MBSF) and the second one uses boosting-based feature learning (BBFL). In our implementations, three types of visual features are used: aspect ratio and size (ARS), histograms of oriented gradients (HOGs), shape profile (SP), representing simple global scale features, statistical features, and global structure features, respectively. The audio features include short time energy (STE), spectral features (SPECs) which consists of spectral energy, entropy, flux and centroid, and perceptual features (PERCs) are Mel-frequency cepstral coefficients (MFFCs) for the perceptual features. The effectiveness of multimodal feature selection is thoroughly studied through empirical studies. The performance between MBSF and BBFL is compared based on our own dataset, which contains over 3000 samples of mainly four types of moving vehicles: sedans, pickup-trucks, vans and buses under various conditions. From this dataset, a subset of 667 samples of multimodal vehicle data is made publicly available at: http://www.cse.ohio-stata.edu/otcbvs-bench/. A number of important observations on the strengths and weakness of those features and their combinations are made as well.

Color CENTRIST: Embedding color information in scene categorization

A new color descriptor has now been proposed to embed color information into the framework of CENsus TRansform hISTogram (CENTRIST), so that such a state-of-the-art visual descriptor can be further improved to categorize image scenes. In the proposed color CENTRIST descriptor, global structure characteristics are described by both gradients derived from intensity values and color variations between image pixels. The spatial pyramid scheme has also been adopted to convey information in different scales. Comprehensive studies based on various datasets were conducted to verify the effectiveness of the color CENTRIST from different aspects, including the way to quantize the color space, selection of color space, and categorization performance on various datasets. We demonstrated that the color CENTRIST descriptor was not only easy to implement, but also reliably achieved superior performance over CENTRIST. An application was also proposed to demonstrate the possibility of applying the color CENTRIST in various domains.