Fusing Vantage Point Trees and Linear Discriminants for Fast Feature Classification

This paper describes a classification strategy that can be regarded as amore general form of nearest-neighbor classification. It fuses the concepts ofnearestneighbor,linear discriminantandVantage-Pointtrees, yielding an efficient indexingdata structure and classification algorithm. In the learning phase, we define a set ofdisjoint subspaces of reduced complexity that can be separated by linear discrimi-nants, ending up with an ensemble of simple (weak) classifiers that work locally. Inclassification, the closest centroids to the query determine the set of classifiers con-sidered, which responses are weighted. The algorithm was experimentally validatedin datasets widely used in the field, attaining error rates that are favorably compara-ble to the state-of-the-art classification techniques. Lastly, the proposed solution hasa set of interesting properties for a broad range of applications: 1) it is determinis-tic; 2) it classifies in time approximately logarithmic with respect to the size of thelearning set, being far more efficient than nearest neighbor classification in terms ofcomputational cost; and 3) it keeps the generalization ability of simple models.info:eu-repo/semantics/publishedVersio

Random Subspace Learning on Outlier Detection and Classification with Minimum Covariance Determinant Estimator

The questions brought by high dimensional data is interesting and challenging. Our study is targeting on the particular type of data in this situation that namely “large p, small n”. Since the dimensionality is massively larger than the number of observations in the data, any measurement of covariance and its inverse will be miserably affected. The definition of high dimension in statistics has been changed throughout decades. Modern datasets with over thousands of dimensions are demanding the ability to gain deeper understanding but hindered by the curse of dimensionality. We decide to review and explore further to negotiate with the curse and extend previous studies to pave a new way for estimating robustness then apply it to outlier detection and classification. We explored the random subspace learning and expand other classification and outlier detection algorithms to adapt its framework. Our proposed methods can handle both high-dimension low-sample size and traditional low-dimensional high-sample size datasets. Essentially, we avoid the computational bottleneck of techniques like Minimum Covariance Determinant (MCD) by computing the needed determinants and associated measures in much lower dimensional subspaces. Both theoretical and computational development of our approach reveal that it is computationally more efficient than the regularized methods in high-dimensional low-sample size, and often competes favorably with existing methods as far as the percentage of correct outlier detection are concerned

Towards Effective Codebookless Model for Image Classification

The bag-of-features (BoF) model for image classification has been thoroughly studied over the last decade. Different from the widely used BoF methods which modeled images with a pre-trained codebook, the alternative codebook free image modeling method, which we call Codebookless Model (CLM), attracted little attention. In this paper, we present an effective CLM that represents an image with a single Gaussian for classification. By embedding Gaussian manifold into a vector space, we show that the simple incorporation of our CLM into a linear classifier achieves very competitive accuracy compared with state-of-the-art BoF methods (e.g., Fisher Vector). Since our CLM lies in a high dimensional Riemannian manifold, we further propose a joint learning method of low-rank transformation with support vector machine (SVM) classifier on the Gaussian manifold, in order to reduce computational and storage cost. To study and alleviate the side effect of background clutter on our CLM, we also present a simple yet effective partial background removal method based on saliency detection. Experiments are extensively conducted on eight widely used databases to demonstrate the effectiveness and efficiency of our CLM method

Content-based image retrieval-- a small sample learning approach.

Tao Dacheng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 70-75).Abstracts in English and Chinese.Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Content-based Image Retrieval --- p.1Chapter 1.2 --- SVM based RF in CBIR --- p.3Chapter 1.3 --- DA based RF in CBIR --- p.4Chapter 1.4 --- Existing CBIR Engines --- p.5Chapter 1.5 --- Practical Applications of CBIR --- p.10Chapter 1.6 --- Organization of this thesis --- p.11Chapter Chapter 2 --- Statistical Learning Theory and Support Vector Machine --- p.12Chapter 2.1 --- The Recognition Problem --- p.12Chapter 2.2 --- Regularization --- p.14Chapter 2.3 --- The VC Dimension --- p.14Chapter 2.4 --- Structure Risk Minimization --- p.15Chapter 2.5 --- Support Vector Machine --- p.15Chapter 2.6 --- Kernel Space --- p.17Chapter Chapter 3 --- Discriminant Analysis --- p.18Chapter 3.1 --- PCA --- p.18Chapter 3.2 --- KPCA --- p.18Chapter 3.3 --- LDA --- p.20Chapter 3.4 --- BDA --- p.20Chapter 3.5 --- KBDA --- p.21Chapter Chapter 4 --- Random Sampling Based SVM --- p.24Chapter 4.1 --- Asymmetric Bagging SVM --- p.25Chapter 4.2 --- Random Subspace Method SVM --- p.26Chapter 4.3 --- Asymmetric Bagging RSM SVM --- p.26Chapter 4.4 --- Aggregation Model --- p.30Chapter 4.5 --- Dissimilarity Measure --- p.31Chapter 4.6 --- Computational Complexity Analysis --- p.31Chapter 4.7 --- QueryGo Image Retrieval System --- p.32Chapter 4.8 --- Toy Experiments --- p.35Chapter 4.9 --- Statistical Experimental Results --- p.36Chapter Chapter 5 --- SSS Problems in KBDA RF --- p.42Chapter 5.1 --- DKBDA --- p.43Chapter 5.1.1 --- DLDA --- p.43Chapter 5.1.2 --- DKBDA --- p.43Chapter 5.2 --- NKBDA --- p.48Chapter 5.2.1 --- NLDA --- p.48Chapter 5.2.2 --- NKBDA --- p.48Chapter 5.3 --- FKBDA --- p.49Chapter 5.3.1 --- FLDA --- p.49Chapter 5.3.2 --- FKBDA --- p.49Chapter 5.4 --- Experimental Results --- p.50Chapter Chapter 6 --- NDA based RF for CBIR --- p.52Chapter 6.1 --- NDA --- p.52Chapter 6.2 --- SSS Problem in NDA --- p.53Chapter 6.2.1 --- Regularization method --- p.53Chapter 6.2.2 --- Null-space method --- p.54Chapter 6.2.3 --- Full-space method --- p.54Chapter 6.3 --- Experimental results --- p.55Chapter 6.3.1 --- K nearest neighbor evaluation for NDA --- p.55Chapter 6.3.2 --- SSS problem --- p.56Chapter 6.3.3 --- Evaluation experiments --- p.57Chapter Chapter 7 --- Medical Image Classification --- p.59Chapter 7.1 --- Introduction --- p.59Chapter 7.2 --- Region-based Co-occurrence Matrix Texture Feature --- p.60Chapter 7.3 --- Multi-level Feature Selection --- p.62Chapter 7.4 --- Experimental Results --- p.63Chapter 7.4.1 --- Data Set --- p.64Chapter 7.4.2 --- Classification Using Traditional Features --- p.65Chapter 7.4.3 --- Classification Using the New Features --- p.66Chapter Chapter 8 --- Conclusion --- p.68Bibliography --- p.7