Novel color and local image descriptors for content-based image search

Content-based image classification, search and retrieval is a rapidly-expanding research area. With the advent of inexpensive digital cameras, cheap data storage, fast computing speeds and ever-increasing data transfer rates, millions of images are stored and shared over the Internet every day. This necessitates the development of systems that can classify these images into various categories without human intervention and on being presented a query image, can identify its contents in order to retrieve similar images. Towards that end, this dissertation focuses on investigating novel image descriptors based on texture, shape, color, and local information for advancing content-based image search. Specifically, first, a new color multi-mask Local Binary Patterns (mLBP) descriptor is presented to improve upon the traditional Local Binary Patterns (LBP) texture descriptor for better image classification performance. Second, the mLBP descriptors from different color spaces are fused to form the Color LBP Fusion (CLF) and Color Grayscale LBP Fusion (CGLF) descriptors that further improve image classification performance. Third, a new HaarHOG descriptor, which integrates the Haar wavelet transform and the Histograms of Oriented Gradients (HOG), is presented for extracting both shape and local information for image classification. Next, a novel three Dimensional Local Binary Patterns (3D-LBP) descriptor is proposed for color images by encoding both color and texture information for image search. Furthermore, the novel 3DLH and 3DLH-fusion descriptors are proposed, which combine the HaarHOG and the 3D-LBP descriptors by means of Principal Component Analysis (PCA) and are able to improve upon the individual HaarHOG and 3D-LBP descriptors for image search. Subsequently, the innovative H-descriptor, and the H-fusion descriptor are presented that improve upon the 3DLH descriptor. Finally, the innovative Bag of Words-LBP (BoWL) descriptor is introduced that combines the idea of LBP with a bag-of-words representation to further improve image classification performance. To assess the feasibility of the proposed new image descriptors, two classification frameworks are used. In one, the PCA and the Enhanced Fisher Model (EFM) are applied for feature extraction and the nearest neighbor classification rule for classification. In the other, a Support Vector Machine (SVM) is used for classification. The classification performance is tested on several widely used and publicly available image datasets. The experimental results show that the proposed new image descriptors achieve an image classification performance better than or comparable to other popular image descriptors, such as the Scale Invariant Feature Transform (SIFT), the Pyramid Histograms of visual Words (PHOW), the Pyramid Histograms of Oriented Gradients (PHOG), the Spatial Envelope (SE), the Color SIFT four Concentric Circles (C4CC), the Object Bank (OB), the Hierarchical Matching Pursuit (HMP), the Kernel Spatial Pyramid Matching (KSPM), the SIFT Sparse-coded Spatial Pyramid Matching (ScSPM), the Kernel Codebook (KC) and the LBP

Large Synoptic Survey Telescope Galaxies Science Roadmap

The Large Synoptic Survey Telescope (LSST) will enable revolutionary studies of galaxies, dark matter, and black holes over cosmic time. The LSST Galaxies Science Collaboration has identified a host of preparatory research tasks required to leverage fully the LSST dataset for extragalactic science beyond the study of dark energy. This Galaxies Science Roadmap provides a brief introduction to critical extragalactic science to be conducted ahead of LSST operations, and a detailed list of preparatory science tasks including the motivation, activities, and deliverables associated with each. The Galaxies Science Roadmap will serve as a guiding document for researchers interested in conducting extragalactic science in anticipation of the forthcoming LSST era

Better support for collaborations preparing for large-scale projects: the case study of the LSST Science Collaborations

Through the lens of the LSST Science Collaborations’ experience, we advocate for new, improved ways to fund large, complex collaborations as they work in preparation for and on peta-scale surveys. We advocate for the establishment of programs to support research and infrastructure that enables innovative collaborative research on such scales

The Herschel-SPIRE Legacy Survey (HSLS): the scientific goals of a shallow and wide submillimeter imaging survey with SPIRE

A large sub-mm survey with Herschel will enable many exciting science opportunities, especially in an era of wide-field optical and radio surveys and high resolution cosmic microwave background experiments. The Herschel-SPIRE Legacy Survey (HSLS), will lead to imaging data over 4000 sq. degrees at 250, 350, and 500 micron. Major Goals of HSLS are: (a) produce a catalog of 2.5 to 3 million galaxies down to 26, 27 and 33 mJy (50% completeness; 5 sigma confusion noise) at 250, 350 and 500 micron, respectively, in the southern hemisphere (3000 sq. degrees) and in an equatorial strip (1000 sq. degrees), areas which have extensive multi-wavelength coverage and are easily accessible from ALMA. Two thirds of the of the sources are expected to be at z > 1, one third at z > 2 and about a 1000 at z > 5. (b) Remove point source confusion in secondary anisotropy studies with Planck and ground-based CMB data. (c) Find at least 1200 strongly lensed bright sub-mm sources leading to a 2% test of general relativity. (d) Identify 200 proto-cluster regions at z of 2 and perform an unbiased study of the environmental dependence of star formation. (e) Perform an unbiased survey for star formation and dust at high Galactic latitude and make a census of debris disks and dust around AGB stars and white dwarfs