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

An innovative spam filtering model based on support vector machine

Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. Many researchers have been trying to separate spam from legitimate emails using machine learning algorithms based on statistical learning methods. In this paper, an innovative and intelligent spam filtering model has been proposed based on support vector machine (SVM). This model combines both linear and nonlinear SVM techniques where linear SVM performs better for text based spam classification that share similar characteristics. The proposed model considers both text and image based email messages for classification by selecting an appropriate kernel function for information transformation.<br /

Bin ratio-based histogram distances and their application to image classification

Large variations in image background may cause partial matching and normalization problems for histogram-based representations, i.e., the histograms of the same category may have bins which are significantly different, and normalization may produce large changes in the differences between corresponding bins. In this paper, we deal with this problem by using the ratios between bin values of histograms, rather than bin values' differences which are used in the traditional histogram distances. We propose a bin ratio-based histogram distance (BRD), which is an intra-cross-bin distance, in contrast with previous bin-to-bin distances and cross-bin distances. The BRD is robust to partial matching and histogram normalization, and captures correlations between bins with only a linear computational complexity. We combine the BRD with the ℓ1 histogram distance and the χ2 histogram distance to generate the ℓ1 BRD and the χ2 BRD, respectively. These combinations exploit and benefit from the robustness of the BRD under partial matching and the robustness of the ℓ1 and χ2 distances to small noise. We propose a method for assessing the robustness of histogram distances to partial matching. The BRDs and logistic regression-based histogram fusion are applied to image classification. The experimental results on synthetic data sets show the robustness of the BRDs to partial matching, and the experiments on seven benchmark data sets demonstrate promising results of the BRDs for image classification

Scene Classification Based on Global Optimized Framework

提出一种基于全局优化策略的场景分类算法.该算法基于整幅图像提取全局场景特征——空间包络特征.从图像块中提取视觉单词,且定义隐变量表示该视觉单词语义,然后引入隐状态结构图描述整幅图像的视觉单词上下文;在场景分类策略上,构造由相容函数组成的目标函数,其中相容函数度量全局场景特征、隐变量与场景类别标记的相容度,通过求解目标函数的全局最优解推断图像的场景类别标记.在标准场景图像库上的对比实验表明该算法优于当前有代表性的场景分类算法.A scene classification algorithm based on global optimized framework is proposed.Firstly, the global scene feature named spatial envelop is obtained from the whole image, the visual word of each image block is extracted, and latent variable is defined to represent the semantic feature of the extracted visual word.Secondly, the structure graph of latent state is introduced to represent the context of visual words.In respect to scene classification strategy, objective function consisting of different potential functions is constructed in which potential functions are defined to measure the relevance of the variables including global scene feature, latent variables and scene category.Finally, the scene category of the image is determined when the global optimized solution of objective function is obtained.The experiments on the standard dataset demonstrate that the proposed algorithm achieves better results than the state-of-the-art algorithms.国家自然科学基金项目(No.41171341); 航空科学基金项目(No.20125168001); 教育部新世纪优秀人才支持计划项目(No.NCET-09-0126); 教育部博士点基金项目(No.20110121110020); 河南省科技创新人才杰出青年项目(No.114100510006); 福建省自然科学基金项目(No.2011J01365); 郑州市科技创新人才培育计划项目(No.10PTGG342-1)资

Estimation of Information Measures and Its Applications in Machine Learning

Information-theoretic measures such as Shannon entropy, mutual information, and the Kullback-Leibler (KL) divergence have a broad range of applications in information and coding theory, statistics, machine learning, and neuroscience. KL-divergence is a measure of the difference between two distributions, while mutual information captures the dependencies between two random variables. Furthermore, the binary Bayes classification error rate specifies the best achievable classifier performance and is directly related to an information divergence measure. In most practical applications the underlying probability distributions are not known and empirical estimation of information measures must be performed based on data. In this thesis, we propose scalable and time-efficient estimators of information measures that can achieve the parametric mean square error (MSE) rate of O(1/N). Our approaches are based on different methods such as k-Nearest Neighbor (k-NN) graphs, Locality Sensitive Hashing (LSH), and Dependence Graphs. The core idea in all of these estimation methods is a unique plug-in estimator of the density ratio of the samples. We prove that the average of an appropriate function of density ratio estimates over all of the points converges to the divergence or mutual information measures. We apply our methods to several machine learning problems such as structure learning, feature selection, and information bottleneck (IB) in deep neural networks.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153472/1/noshad_1.pd