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Eminence of learning algorithm applied for computer vision tasks depends on the features engineered from image. It's premise that different representations can interweave and ensnare most of the elucidative genes that are responsible for variations in images, be it rigid, affine or projective. Hence researches give at most attention in hand-engineering features that capture these variations. But problem...
A heuristic utilizing both spectral and spatial information is proposed for active learning. It addresses the issue of iteratively querying most informative training samples with a special focus on spatial-contextual image classification. With the aim to utilize all information during the learning process, the proposed heuristic queries unlabeled pixels considering spectral-spatial inconsistency (SSI),...
Remote sensing has much to gain from citizen sensing. This is particularly evident in relation to the provision of ground reference data for use in the training and testing stages of supervised image classification analyses used to generate thematic maps from remotely sensed data. Citizens are able to provide data over large geographical areas inexpensively, addressing potential problems connected...
This paper addresses the problem of parameter optimization for Markov random field (MRF) models for supervised classification of remote sensing images. MRF model parameters generally impact on classification accuracy, and their automatic optimization is still an open issue especially in the supervised case. The proposed approach combines a mean square error (MSE) formulation with Platt's sequential...
A CSR-Based (Contextual Sparse Representation) classification method for PolSAR image is proposed based on the idea of sparse representation and spatial correlation, which incorporates the intrinsic polarimetric information and the spatial contextual information in the sparse representation procedure. Firstly, multiple useful features are extracted to describe PolSAR images at various aspects. Then,...
The quality of the training data used in a supervised image classification can impact on the accuracy of the resulting thematic map obtained. Here the effects of mis-labeled training cases on the accuracy of classifications by discriminant analysis and a support vector machine were explored. The accuracy of both classifiers varied with the amount and nature of mis-labeled training cases. In particular,...
Recently, the sparse coding based image representation has achieved state-of-the-art recognition results on many benchmarks. In this paper, we propose Multi-cue Normalized Non-Negative Sparse Encoder (MN3SE) which enforces both the non-negative constraint and the shift-invariant constraint on top of the traditional sparse coding criteria, and takes multi-cue to further boost the performance. The former...
Spatial pyramid (SP) representation is an extension of bag-of-feature model which embeds spatial layout information of local features by pooling feature codes over pre-defined spatial shapes. However, the uniform style of spatial pooling shapes used in standard SP is an ad-hoc manner without theoretical motivation, thus lacking the generalization power to adapt to different distribution of geometric...
Gabor features have been proved to be effective for the recently-proposed nearest regularized subspace (NRS) classifier. In this paper, we further investigate a residual fusion based strategy with multiple features and NRS. Multiple features include local binary patterns (LBP), Gabor features and the original spectral signatures. In the proposed classification framework, each type of feature is first...
In this paper, we propose a new semi-supervised classification algorithm called RDE_self-training, which is an automatic framework for classification of remotely sensed hyperspectral images. The algorithm exploits abundant unlabeled samples when the number of labeled samples is limited to learn an accurate classifier. Train the classifier iteratively on enlarged training set with data editing. Firstly,...
Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the human body makes classification difficult. “Deep learning” methods such as convolutional networks (ConvNets) outperform other state-of-the-art methods in image classification tasks. In this work, we present a method for organ-...
The general phenomenon for Image Classification is based on the Feature extraction mechanism. In every domain of image analysis, the classification accuracy is dependent on how better the feature set is generated which helps the machine to learn and predict the unknown sample class label. In this paper, a novel feature extraction mechanism is proposed and named as Counting Label Occurrence Matrix...
Hyper spectral image processing is becoming an active topic in remote sensing and other applications in current times. Hyper spectral images can easily distinguish materials which are spectrally similar. Many techniques are available to classify hyper spectral images which are mainly deals with the curse of dimensionality and working with few training data issues which confront during classification...
Content based classification approach is becoming necessary to support the retrieval and indexing of images. This paper uses Color features of an image to form a feature vector on which data pre-processing is applied. These features are then used by machine learning classifiers to classify the images. Classification accuracy is evaluated in two color spaces and image sizes. Empirical results show...
The bag-of-features based models are widely used for image classification. In these models, an image is represented as a set of visual words which come from a dictionary. Therefore, a well learned dictionary is responsible for the discriminative power of representations of images. Our observations show that the representation of an image carries rich underlying information of a dictionary, so we propose...
The aim of this paper is to develop an effective classification approach based on Random Forest (RF) algorithm. Three fruits; i.e., apples, Strawberry, and oranges were analysed and several features were extracted based on the fruits' shape, colour characteristics as well as Scale Invariant Feature Transform (SIFT). A preprocessing stages using image processing to prepare the fruit images dataset...
As a new learning framework, Multi-Instance learning is used successfully in vision classification and labeled recently. In this paper, a novel Multi-instance bag generating method is put forward on the basis of a Gaussian Mixed Model. The generated GMM model composes not only color but also the locally stable unchangeable components. It is called MI bag by researchers. Besides this, another method...
Abstract-Image annotation has been identified to be a suitable means by which the semantic gap which has made the accuracy of Content-based image retrieval unsatisfactory be eliminated. However existing methods of automatic annotation of images depends on supervised learning, which can be difficult to implement due to the need for manually annotated training samples which are not always readily available...
This paper discusses the evaluation of two supervised learning based image classification algorithms. The classification subject of this work is part of a complete vision based road sign recognition system to be implanted using the VHDL language on an FPGA card for driver assistance applications. The classification is used in order to classify road scene images into different day times according to...
Spatial partitioning is proven to be beneficial for the tasks of image classification, scene categorization and object recognition. The most popular method to capture rough spatial structure of the scene is spatial pyramid matching. However, spatial pyramid matching results in an image representation that is sensitive to rotations. In this research we investigate the influence of upright and rotated...
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