Novel image descriptors and learning methods for image classification applications

Image classification is an active and rapidly expanding research area in computer vision and machine learning due to its broad applications. With the advent of big data, the need for robust image descriptors and learning methods to process a large number of images for different kinds of visual applications has greatly increased. Towards that end, this dissertation focuses on exploring new image descriptors and learning methods by incorporating important visual aspects and enhancing the feature representation in the discriminative space for advancing image classification. First, an innovative sparse representation model using the complete marginal Fisher analysis (CMFA-SR) framework is proposed for improving the image classification performance. In particular, the complete marginal Fisher analysis method extracts the discriminatory features in both the column space of the local samples based within class scatter matrix and the null space of its transformed matrix. To further improve the classification capability, a discriminative sparse representation model is proposed by integrating a representation criterion such as the sparse representation and a discriminative criterion. Second, the discriminative dictionary distribution based sparse coding (DDSC) method is presented that utilizes both the discriminative and generative information to enhance the feature representation. Specifically, the dictionary distribution criterion reveals the class conditional probability of each dictionary item by using the dictionary distribution coefficients, and the discriminative criterion applies new within-class and between-class scatter matrices for discriminant analysis. Third, a fused color Fisher vector (FCFV) feature is developed by integrating the most expressive features of the DAISY Fisher vector (D-FV) feature, the WLD-SIFT Fisher vector (WS-FV) feature, and the SIFT-FV feature in different color spaces to capture the local, color, spatial, relative intensity, as well as the gradient orientation information. Furthermore, a sparse kernel manifold learner (SKML) method is applied to the FCFV features for learning a discriminative sparse representation by considering the local manifold structure and the label information based on the marginal Fisher criterion. Finally, a novel multiple anthropological Fisher kernel framework (M-AFK) is presented to extract and enhance the facial genetic features for kinship verification. The proposed method is derived by applying a novel similarity enhancement approach based on SIFT flow and learning an inheritable transformation on the multiple Fisher vector features that uses the criterion of minimizing the distance among the kinship samples and maximizing the distance among the non-kinship samples. The effectiveness of the proposed methods is assessed on numerous image classification tasks, such as face recognition, kinship verification, scene classification, object classification, and computational fine art painting categorization. The experimental results on popular image datasets show the feasibility of the proposed methods

Measuring reproducibility of high-throughput experiments

Reproducibility is essential to reliable scientific discovery in high-throughput experiments. In this work we propose a unified approach to measure the reproducibility of findings identified from replicate experiments and identify putative discoveries using reproducibility. Unlike the usual scalar measures of reproducibility, our approach creates a curve, which quantitatively assesses when the findings are no longer consistent across replicates. Our curve is fitted by a copula mixture model, from which we derive a quantitative reproducibility score, which we call the "irreproducible discovery rate" (IDR) analogous to the FDR. This score can be computed at each set of paired replicate ranks and permits the principled setting of thresholds both for assessing reproducibility and combining replicates. Since our approach permits an arbitrary scale for each replicate, it provides useful descriptive measures in a wide variety of situations to be explored. We study the performance of the algorithm using simulations and give a heuristic analysis of its theoretical properties. We demonstrate the effectiveness of our method in a ChIP-seq experiment.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS466 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Approximate selective inference via maximum likelihood

This article considers a conditional approach to selective inference via approximate maximum likelihood for data described by Gaussian models. There are two important considerations in adopting a post-selection inferential perspective. While one of them concerns the effective use of information in data, the other aspect deals with the computational cost of adjusting for selection. Our approximate proposal serves both these purposes-- (i) exploits the use of randomness for efficient utilization of left-over information from selection; (ii) enables us to bypass potentially expensive MCMC sampling from conditional distributions. At the core of our method is the solution to a convex optimization problem which assumes a separable form across multiple selection queries. This allows us to address the problem of tractable and efficient inference in many practical scenarios, where more than one learning query is conducted to define and perhaps redefine models and their corresponding parameters. Through an in-depth analysis, we illustrate the potential of our proposal and provide extensive comparisons with other post-selective schemes in both randomized and non-randomized paradigms of inference

(In)Efficient management of interacting environmental bads

Many environmental problems involve the transformation of multiple harmful substances into one or more damage agents much in the same way as a firm transforms inputs into outputs. Yet environmental management differs from a firm's production in one important respect: while a firm seeks efficient input allocation to maximize profit, an environmental planner allocates abatement efforts to render the production of damage agents as inefficient as possible. We characterize a solution to the hmultiple pollutants problem and show that the optimal policy is often a corner solution, in which abatement is focused on a single pollutant. Corner solutions may arise even in well-behaved problems with concave production functions and convex damage and cost functions. Furthermore, even concentrating on a wrong pollutant may yield greater net benefits than setting uniform abatement targets for a harmful substances. Our general theoretical results on the management of flow and stock pollutants are complemented by two numerical examples illustrating the abatement of eutrophying nutrients and greenhouse gases

Productivity comparisons: the european union agriculture

This paper ail11s at l11easuring the total factor productivity (TFP) of the European agricultural finns. With a Translog index, an interspatial comparison of tIle twelve European countries and intertel11poral productivity variations are computed to l11easure the different rate of TFP (Translog, Fisher and Hulten indexes) in the European firms. The approach that we use is to calculate non parametric indexex of total factor productivity which allow flexible l110delling of underlying technology and easy calculation from the account data of the firms. The implication of the quasi-fix family work factor for the short mn and long mn equilibrium of the firms differ between countries and has consequences on the TFP path. The final cornments offer some explanation according with theory available

Cost-benefit analysis of ecological networks assessed through spatial analysis of ecosystem services

1.The development of ecological networks could enhance the ability of species to disperse across fragmented landscapes and could mitigate against the negative impacts of climate change. The development of such networks will require widespread ecological restoration at the landscape scale, which is likely to be costly. However, little information is available regarding the cost-effectiveness of restoration approaches. 2.We address this knowledge gap by examining the potential impact of landscape-scale habitat restoration on the value of multiple ecosystem services across the catchment of the River Frome in Dorset, England. This was achieved by mapping the market value of four ecosystem services (carbon storage, crops, livestock and timber) under three different restoration scenarios, estimating restoration costs, and calculating net benefits. 3.The non-market value of additional services (cultural, aesthetic and recreational value) was elicited from local stakeholders using an online survey tool. Flood risk was assessed using a scoring approach. Spatial Multi-Criteria Analysis (MCA) was conducted, incorporating both market and non-market values, to evaluate the relative benefits of restoration scenarios. These were compared with impacts of restoration on biodiversity value. 4.Multi-Criteria Analysis results consistently ranked restoration scenarios above a non-restoration comparator, reflecting the increased provision of multiple ecosystem services. Restoration scenarios also provided benefits to biodiversity, in terms of increased species richness and habitat connectivity. However, restoration costs consistently exceeded the market value of ecosystem services. 5.Synthesis and applications. Establishment of ecological networks through ecological restoration is unlikely to deliver net economic benefits in landscapes dominated by agricultural land use. This reflects the high costs of ecological restoration in such landscapes. The cost-effectiveness of ecological networks will depend on how the benefits provided to people are valued, and on how the value of non-market benefits are weighted against the costs of reduced agricultural and timber production. Future plans for ecological restoration should incorporate local stakeholder values, to ensure that benefits to people are maximised. © 2012 The Authors. Journal of Applied Ecology © 2012 British Ecological Society