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Image classification is a method that distinguishes the different categories of targets based on the different features of image. The current problem usually is that the feature modeling of target has a great influence on recognition robustness. In order to solve this problem, a correlation-based method is presented to optimize the bag-of-visual-word (BOVW) model by reducing the dictionary size. The...
There has been a great interest in the systems that predict clinical labels from the brain images automatically for the last decade since it is a very important task that helps clinicians for decision making. In this study, clinical labels of the structural brain magnetic resonance (MR) images are predicted automatically using the random forests ensemble method. Morphological measurements like volume...
Due to the high spectral resolution and the similarity of some spectrums between different classes, hyperspectral image classification turns out to be an important but challenging task. Researches show the powerful ability of deep learning for hyperspectral image classification. However, the lack of training samples makes it difficult to extract discriminative features and achieve performance as expected...
Every organism emits energy around it which comprises UV-radiation, EM-radiation, infrared and thermal radiation. This energy around human body represents health condition of the subject under study. These energy fields are called as aura of the body under consideration. Several types of equipments are there to capture such energy. Kirlian camera captures the distribution of energy radiation around...
In recent years, intelligent mathematics problem solving has aroused the interest of researchers. In the intelligent mathematics problem solving system related to high school, the classification of statistical graph is a key step. Consequently, the classification of statistical graphs has become an urgent problem to be solved. In this paper, a new method is proposed for statistical graphs classification...
Multiple support vector machines (SVMs) with random subspaces [1]-[5] have been performing excellently for hyperspectral image classification to reduce the correlation between features and avoid the Hughes phenomena. In most random subspace methods, features were randomly selected without replacement from the original feature set according to uniform distribution [6]. However, in general, SVM with...
Artificial neural networks (ANNs) have been widely used in the analysis of remotely sensed imagery. In particular, convolutional neural networks (CNNs) are gaining more and more attention. Unlike traditional CNNs methods, where the relevant information to classify the elements of a remotely sensed image is extracted only from the last fully-connected layer, the new adaptive deep pyramid matching (ADPM)...
Convolutional neural networks (CNNs), widely studied in the domain of computer vision, are more recently finding application in the analysis of high-resolution aerial and satellite imagery. In this paper, we investigate a deep feature learning approach based on CNNs for the detection of informal settlements in Dar es Salaam, Tanzania. This information is vital for decision making and planning of upgrading...
Human action recognition is an imperative research area in the field of computer vision due to its numerous applications. Recently, with the emergence and successful deployment of deep learning techniques for image classification, object recognition, and speech recognition, more research is directed from traditional handcrafted to deep learning techniques. This paper presents a novel method for human...
The intent of the image classification process is to objectively categorize an image visual contents into semantic meanings. The classification process is a challenging task due to the difficulty associated with extracting and identifying relevant shape information. In this paper, we introduce a new fusion algorithm that combines the strengths of deep learning and mid-level image descriptors. Our...
Histopathology image classification can provide automated support towards cancer diagnosis. In this paper, we present a transfer learning-based approach for histopathology image classification. We first represent the image feature by Fisher Vector (FV) encoding of local features that are extracted using the Convolutional Neural Network (CNN) model pretrained on ImageNet. Next, to better transfer the...
Hereby in this paper, we are interested to extraction methods and classification in case of image classification and recognition application. We expose the performance of training models on varying classifier algorithms on Caltech 101 images categories. For feature extraction functions we evaluate the use of the classical SURF technique against global color feature extraction. The purpose of our work...
At present, collaborative representation based classification (CRC) is widely used in many pattern classification and recognition tasks. Meanwhile, spatial pyramid matching (SPM) method, which considers the spatial information in representing the image, is efficient for image classification. However, for SPM, the weights to evaluate the representation of different subregions are fixed. In this paper,...
We consider the use of transfer learning, via the use of deep Convolutional Neural Networks (CNN) for the image classification problem posed within the context of X-ray baggage security screening. The use of a deep multi-layer CNN approach, traditionally requires large amounts of training data, in order to facilitate construction of a complex complete end-to-end feature extraction, representation...
In this work we address the multispectral image classification problem from a Bayesian perspective. We develop an algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian (SG) priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns. The proposed algorithm...
Localizing seismic structures that can form traps for hydrocarbon reservoirs within large seismic volumes is a very challenging task. Due to the lack of accurately labeled data, we propose a weakly-supervised model for labeling seismic volumes using only a few labeled exemplars. Using six manually-labeled patches, we are able to extract patches that contain instances of similar geophysical structures...
In this paper, a novel kernel low rank representation (KLRR) method for hyperspectral image classification is proposed. Firstly, we extract the global structure characteristics information of the hyperspectral image based on low rank representation (LRR), then use it as a prior to constrain the recovery coefficient matrix. In order to further improve the classification efficiency and deal with the...
This paper proposes an ideal regularized composite kernel (IRCK) framework for hyperspectral images (HSI) classification. In learning a composite kernel, IRCK exploits spectral information, spatial information, and label information simultaneously. It incorporates the labels into standard spectral and spatial kernels by means of ideal kernel according to a regularization kernel learning framework,...
In this paper, a novel feature extraction framework is proposed for hyperspectral image classification. Inspired by the role of discriminant function in classifier, which intends to learn a mapping from the input features to label information in class space, we develop a feature extraction framework to learn the new feature representation of original input features in class space, by establishing...
Conditional Random Field(CRF) has been successfully applied to the hyperspectral image classification. However, it suffers from the availability of large amount of labeled pixels, which is labor- and time-consuming to obtain in practice. In this paper, a semi-supervised CRF(ssCRF) is proposed for hyperspectral image classification with limited labeled pixels. Laplacian Support Vector Machine(LapSVM),...
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