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This paper introduces a method for in-situ underwater target classification, based on an image retrieval system, that can be implemented using a simple two-layer kernel-based network. This system incorporates a learning mechanism that captures new information for discriminating between objects in different classes or within the same class from a set of input-output pairs with associated confidence...
Nowadays very large archives of digital images can be easily produced thanks to the availability of digital cameras as standalone devices, or embedded into a number of portable devices. Each personal computer is typically a repository for thousands of images, while the Internet can be seen as a very large repository. One of the most severe problems in the classification and retrieval of images from...
The support vector machines (SVMs), as one of special regularization methods, has been used successfully in the field of pattern recognition. However, the traditional SVMs, a supervised learning method, gets the normal vector of the decision boundary mainly according to the largest interval law but has not taken the underlying geometric structure and the discriminant information into full consideration...
A multi-view based active learning method (AMDWVE) is proposed as a means to optimally construct the training set for supervised classification of hyperspectral data, thereby reducing the effort required to acquire ground reference data. The method explores the intrinsic multi-view information embedded in hyperspectral data. By adaptively and quantitatively measuring the disagreement level of different...
This paper addresses the recent trends in machine learning methods for the automatic classification of remote sensing (RS) images. In particular, we focus on two new paradigms: semisupervised and active learning. These two paradigms allow one to address classification problems in the critical conditions where the available labeled training samples are limited. These operational conditions are very...
In this work, we present a new support vector machine (SVM)-based active learning method for the classification of remote sensing images. Starting from an initial suboptimal training set, an iterative process defines the regions of significance in the feature space, then selects additional samples from a large set of unlabeled data and adds them to the training set after their manual labeling. Experimental...
Multiple instance learning is a recently researched learning paradigm that allows a machine learning algorithm to learn target concepts with uncertainty in the class labels of training data. In the following, this approach is assessed for use in hyperspectral image analysis. Two leading MIL algorithms are used in a classification experiment and results are compared to a state-of-the-art context-based...
A local proximity based data regularization framework for active learning is proposed as a means to optimally construct the training set for supervised classification of hyperspectral data, thereby reducing the effort required to acquire ground reference data. Based on the "Consistency Assumption", a local k-nearest neighborhood Laplacian Graph based regularizer is constructed to explore...
Classification is one of the most important procedures in high-resolution remotely sensed image information extraction. This paper introduced Adaboost-SVM algorithm to IKONOS image classification. The classification performance of Adabost-SVM and single SVM were quantitatively analyzed and qualitatively evaluated. The results show that: In the case of small training samples, Adaboost-SVM outperforms...
Human detection is a task met in many applications such as surveillance & security, safe driving system, intelligent vehicles, etc.. It is more complicated compared to the detection of car and other targets, due to the variety of poses and external appearance of human bodies. This paper presents an adaboost human detection algorithm and its implementation on a DSP platform. The detector uses haar-like...
In image categorization the goal is to decide if an image belongs to a certain category or not. A binary classifier can be learned from manually labeled images; while using more labeled examples improves performance, obtaining the image labels is a time consuming process. We are interested in how other sources of information can aid the learning process given a fixed amount of labeled images. In particular,...
The traditional SPM approach based on bag-of-features (BoF) requires nonlinear classifiers to achieve good image classification performance. This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM. LLC utilizes the locality constraints to project each descriptor into its local-coordinate system, and the projected...
The k-nearest neighbor (K-NN) framework was successfully used for tasks of computer vision. In image categorization, k-NN is an important and significant rule. However, two major problems usually affect this rule: the NN classifier vote and the metric employed to compute the distance between neighbors. This paper deals with both. First, a boosting k-NN algorithm learns the coefficients of weak classifiers,...
This paper presents a new online multi-classifier boosting algorithm for learning object appearance models. In many cases the appearance model is multi-modal, which we capture by training and updating multiple strong classifiers. The proposed algorithm jointly learns the classifiers and a soft partitioning of the input space, defining an area of expertise for each classifier. We show how this formulation...
Automatic categorization of videos in a Web-scale unconstrained collection such as YouTube is a challenging task. A key issue is how to build an effective training set in the presence of missing, sparse or noisy labels. We propose to achieve this by first manually creating a small labeled set and then extending it using additional sources such as related videos, searched videos, and text-based webpages...
A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in order to redetect objects over succeeding frames. Although these methods usually deliver excellent results and run in real-time they also tend to drift in case of wrong updates during the self-learning process. Recent approaches tackled this problem by formulating tracking-by-detection as either one-shot...
Visual understanding is often based on measuring similarity between observations. Learning similarities specific to a certain perception task from a set of examples has been shown advantageous in various computer vision and pattern recognition problems. In many important applications, the data that one needs to compare come from different representations or modalities, and the similarity between such...
The design of feature spaces for local image descriptors is an important research subject in computer vision due to its applicability in several problems, such as visual classification and image matching. In order to be useful, these descriptors have to present a good trade off between discriminating power and robustness to typical image deformations. The feature spaces of the most useful local descriptors...
Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled approach to combine multiple cues, and to obtain state-of-the-art performance. A general drawback of these strategies is the high computational cost during training, that prevents their application to large-scale problems. They also do not provide theoretical guarantees on their convergence rate...
Multiple instance (MI) learning is a recent learning paradigm that is more flexible than standard supervised learning algorithms in the handling of label ambiguity. It has been used in a wide range of applications including image classification, object detection and object tracking. Typically, MI algorithms are trained in a batch setting in which the whole training set has to be available before training...
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