Multiple kernel active learning for image classification

Recently, multiple kernel learning (MKL) methods have shown promising performance in image classification. As a sort of supervised learning, training MKL-based classifiers relies on selecting and annotating extensive dataset. In general, we have to manually label large amount of samples to achieve desirable MKL-based classifiers. Moreover, MKL also suffers a great computational cost on kernel computation and parameter optimization. In this paper, we propose a local adaptive active learning (LA-AL) method to reduce the labeling and computational cost by selecting the most informative training samples. LA-AL adopts a top-down (or global-local) strategy for locating and searching informative samples. Uncertain samples are first clustered into groups, and then informative samples are consequently selected via inter-group and intra-group competitions. Experiments over COREL-5K show that the proposed LA-AL method can significantly reduce the demand of sample labeling and have achieved the state-of-the-art performance. ?2009 IEEE.EI

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

Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods

Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. The framework of statistical learning has gained popularity in the last decade. New methods have been presented to account for the spatial homogeneity of images, to include user's interaction via active learning, to take advantage of the manifold structure with semisupervised learning, to extract and encode invariances, or to adapt classifiers and image representations to unseen yet similar scenes. This tutuorial reviews the main advances for hyperspectral remote sensing image classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201

Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain where large volumes of labelled data are difficult to obtain due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data. Our framework has three contributions: (i) We extend kernel learning to identify and represent invariant features across image sub-patches in an unsupervised manner. (ii) We initialise our kernel learning with a layer-wise pre-training scheme that leverages the sparsity inherent in medical images to extract initial discriminative features. (iii) We adapt a multi-scale spatial pyramid pooling (SPP) framework to capture subtle geometric differences between learned visual features. We evaluated our framework in medical image retrieval and classification on three public datasets. Our results show that our CSKN had better accuracy when compared to other conventional unsupervised methods and comparable accuracy to methods that used state-of-the-art supervised convolutional neural networks (CNNs). Our findings indicate that our unsupervised CSKN provides an opportunity to leverage unannotated big data in medical imaging repositories.Comment: Accepted by Medical Image Analysis (with a new title 'Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis'). The manuscript is available from following link (https://doi.org/10.1016/j.media.2019.06.005

Sparsity-accuracy trade-off in MKL

We empirically investigate the best trade-off between sparse and uniformly-weighted multiple kernel learning (MKL) using the elastic-net regularization on real and simulated datasets. We find that the best trade-off parameter depends not only on the sparsity of the true kernel-weight spectrum but also on the linear dependence among kernels and the number of samples.Comment: 8pages, 2 figure