Combining multiple resolutions into hierarchical representations for kernel-based image classification

Geographic object-based image analysis (GEOBIA) framework has gained increasing interest recently. Following this popular paradigm, we propose a novel multiscale classification approach operating on a hierarchical image representation built from two images at different resolutions. They capture the same scene with different sensors and are naturally fused together through the hierarchical representation, where coarser levels are built from a Low Spatial Resolution (LSR) or Medium Spatial Resolution (MSR) image while finer levels are generated from a High Spatial Resolution (HSR) or Very High Spatial Resolution (VHSR) image. Such a representation allows one to benefit from the context information thanks to the coarser levels, and subregions spatial arrangement information thanks to the finer levels. Two dedicated structured kernels are then used to perform machine learning directly on the constructed hierarchical representation. This strategy overcomes the limits of conventional GEOBIA classification procedures that can handle only one or very few pre-selected scales. Experiments run on an urban classification task show that the proposed approach can highly improve the classification accuracy w.r.t. conventional approaches working on a single scale.Comment: International Conference on Geographic Object-Based Image Analysis (GEOBIA 2016), University of Twente in Enschede, The Netherland

Automatic document classification of biological literature

Background: Document classification is a wide-spread problem with many applications, from organizing search engine snippets to spam filtering. We previously described Textpresso, a text-mining system for biological literature, which marks up full text according to a shallow ontology that includes terms of biological interest. This project investigates document classification in the context of biological literature, making use of the Textpresso markup of a corpus of Caenorhabditis elegans literature. Results: We present a two-step text categorization algorithm to classify a corpus of C. elegans papers. Our classification method first uses a support vector machine-trained classifier, followed by a novel, phrase-based clustering algorithm. This clustering step autonomously creates cluster labels that are descriptive and understandable by humans. This clustering engine performed better on a standard test-set (Reuters 21578) compared to previously published results (F-value of 0.55 vs. 0.49), while producing cluster descriptions that appear more useful. A web interface allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept. Conclusions: We have demonstrated a simple method to classify biological documents that embodies an improvement over current methods. While the classification results are currently optimized for Caenorhabditis elegans papers by human-created rules, the classification engine can be adapted to different types of documents. We have demonstrated this by presenting a web interface that allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept

The Future of Systematics: Tree-Thinking Without the Tree

Phylogenetic trees are meant to represent the genealogical history of life and apparently derive their justification from the existence of the tree of life and the fact that evolutionary processes are tree-like. However, there are a number of problems for these assumptions. Here it is argued that once we understand the important role that phylogenetic trees play as models which contain idealizations, we can accept these criticisms and deny the reality of the tree while justifying the continued use of trees in phylogenetic theory and preserving nearly all of what defenders of trees have called “the importance of tree-thinking.

An enhanced classification of artificial ground

This report describes a detailed scheme for the mapping and recording of artificial ground. It presents codes and descriptions that underpin the entries in the British Geological Survey stratigraphical lexico

Bianchi Model CMB Polarization and its Implications for CMB Anomalies

We derive the CMB radiative transfer equation in the form of a multipole hierarchy in the nearly-Friedmann-Robertson-Walker limit of homogeneous, but anisotropic, universes classified via their Bianchi type. Compared with previous calculations, this allows a more sophisticated treatment of recombination, produces predictions for the polarization of the radiation, and allows for reionization. Our derivation is independent of any assumptions about the dynamical behaviour of the field equations, except that it requires anisotropies to be small back to recombination; this is already demanded by observations. We calculate the polarization signal in the Bianchi VIIh case, with the parameters recently advocated to mimic the several large-angle anomalous features observed in the CMB. We find that the peak polarization signal is ~ 1.2 micro K for the best-fit model to the temperature anisotropies, and is mostly confined to multipoles l<10. Remarkably, the predicted large-angle EE and TE power spectra in the Bianchi model are consistent with WMAP observations that are usually interpreted as evidence of early reionization. However, the power in B-mode polarisation is predicted to be similar to the E-mode power and parity-violating correlations are also predicted by the model; the WMAP non-detection of either of these signals casts further strong doubts on the veracity of attempts to explain the large-angle anomalies with global anisotropy. On the other hand, given that there exist further dynamical degrees of freedom in the VIIh universes that are yet to be compared with CMB observations, we cannot at this time definitively reject the anisotropy explanation.Comment: Accepted for publication in MNRAS. Minor grammatical and typographical changes to reflect version in press. 13 pages, 6 figure