In this paper, we have developed a new scheme for achieving multilevel annotations of large-scale images automatically. To achieve more sufficient representation of various visual properties of the images, both the global visual features and the local visual features are extracted for image content representation. To tackle the problem of huge intraconcept visual diversity, multiple types of kernels are integrated to characterize the diverse visual similarity relationships between the images more precisely, and a multiple kernel learning algorithm is developed for SVM image classifier training. To address the problem of huge interconcept visual similarity, a novel multitask learning algorithm is developed to learn the correlated classifiers for the sibling image concepts under the same parent concept and enhance their discrimination and adaptation power significantly. To tackle the problem of huge intraconcept visual diversity for the image concepts at the higher levels of the concept ontology, a novel hierarchical boosting algorithm is developed to learn their ensemble classifiers hierarchically. In order to assist users on selecting more effective hypotheses for image classifier training, we have developed a novel hyperbolic framework for large-scale image visualization and interactive hypotheses assessment. Our experiments on large-scale image collections have also obtained very positive results.

An efficient Fe(2)O(3)-pillared rectorite (Fe-R) clay was successfully developed as a heterogeneous catalyst for photo-Fenton degradation of organic contaminants. X-ray diffraction analysis and high-resolution transmission electron microscope analysis clearly showed the existence of the Fe(2)O(3) nanoparticles in the Fe-R catalyst. The catalytic activity of the Fe-R catalyst was evaluated by the discoloration and chemical oxygen demand (COD) removal of an azo-dye rhodamine B (RhB, 100 mg/L) and a typical persistent organic pollutant 4-nitrophenol (4-NP, 50 mg/L) in the presence of hydrogen peroxide (H(2)O(2)) under visible light irradiation (lambda > 420 nm). It was found that the discoloration rate of the two contaminants was over 99.3%, and the COD removal rate of the two contaminants was over 87.0%. The Fe-R catalyst showed strong adsorbability for the RhB in the aqueous solution. Moreover, the Fe-R catalyst still showed good stability for the degradation of RhB after five recycles. Zeta potential and Fourier transform infrared spectroscopy were used to examine the photoreaction processes. Finally, a possible photocatalytic mechanism was proposed.

An analytical model for plasmon modes in graphene-coated dielectric nanowire is presented. Plasmon modes could be classified by the azimuthal field distribution characterized by a phase factor exp(imφ) in the electromagnetic field expression and eigen equation of dispersion relation for plasmon modes is derived. The characteristic of plasmon modes could be tuned by changing nanowire radius, dielectric permittivity of nanowire and chemical potential of graphene. The proposed model provides a fast insight into the mode behavior of graphene-coated nanowire, which would be useful for applications based on graphene plasmonics in cylindrical waveguide.

Automatic image annotation is a promising solution to enable semantic image retrieval via keywords. In this paper, we propose a multi-level approach to annotate the semantics of natural scenes by using both the dominant image components (salient objects) and the relevant semantic concepts. To achieve automatic image annotation at the content level, we use salient objects as the dominant image components for image content representation and feature extraction. To support automatic image annotation at the concept level, a novel image classification technique is developed to map the images into the most relevant semantic image concepts. In addition, Support Vector Machine (SVM) classifiers are used to learn the detection functions for the pre-defined salient objects and finite mixture models are used for semantic concept interpretation and modeling. An adaptive EM algorithm has been proposed to determine the optimal model structure and model parameters simultaneously. We have also demonstrated that our algorithms are very effective to enable multi-level annotation of natural scenes in a largescale image dataset.

We propose in this Letter a single-mode graphene-coated nanowire surface plasmon waveguide. The single-mode condition and modal cutoff wavelength of high order modes are derived from an analytic model and confirmed by numerical simulation. The mode number diagram of the proposed waveguide in the wavelength-radius space is also demonstrated. By changing the Fermi level of graphene, the performance of the proposed waveguide could be tuned flexibly, offering potential application in tunable nanophotonic devices.

The performance of image classifiers largely depends on two inter-related issues: (1) suitable frameworks for image content representation and automatic feature extraction; (2) effective algorithms for image classifier training and feature subset selection. To address the first issue, a multiresolution grid-based framework is proposed for image content representation and feature extraction to bypass the time-consuming and erroneous process for image segmentation. To address the second issue, a hierarchical boosting algorithm is proposed by incorporating feature hierarchy and boosting to scale up SVM image classifier training in high-dimensional feature space. The high-dimensional multi-modal heterogeneous visual features are partitioned into multiple low-dimensional single-modal homogeneous feature subsets and each of them characterizes certain visual property of images. For each homogeneous feature subset, principal component analysis (PCA) is performed to exploit the feature correlations and a weak classifier is learned simultaneously. After the weak classifiers for different feature subsets and grid sizes are available, they are combined to boost an optimal classifier for the given object class or image concept, and the most representative feature subsets and grid sizes are selected. Our experiments on a specific domain of natural images have obtained very positive results.

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