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In this paper, A novel classification approach based on sparse representation framework is proposed. The method finds the minimum Euclidian distance between an input patch (pattern) and atoms (templates) of a learnt-base dictionary for different classes to perform the classification task. A mathematical approach is developed to map the sparse representation vector to Euclidian distances. We show that...
This paper introduces a classification system for remote sensing ASTER satellite imagery using SVM and particle swarm optimization (PSO) algorithm. The proposed system starts with the identification of selected area of study. This is followed by a pre-processing phase using mapping polynomial algorithm as geometric correction. Followed by, applying threshold algorithm for image segmentation. Then...
Massive amount of data with high dimensionality can pose a problem for efficient image classification. Recently there has been an effort to extend the application of sparse representations of signals to image classification. In this paper, we propose a method that extracts the smallest number of features that discriminate the images from different classes using a cost function that combines discrimination...
Support Vector Machine (SVM) was used in the Genetic Algorithms (GA) process to select and classify a subset of hyperspectral image bands. The method was applied to fluorescence hyperspectral data for the detection of aflatoxin contamination in Aspergillus flavus infected single corn kernels. In the band selection process, the training sample classification accuracy was used as fitness function. Two...
Image classification is a complex but important task especially in the areas of machine vision and image analysis such as remote sensing and face recognition. One of the challenges in image classification is finding an optimal set of features for a particular task because the choice of features has direct impact on the classification performance. However the goodness of a feature is highly problem...
Hyperspectral imaging systems provide high spectral information content by acquiring data in hundreds of narrow spectral bands. This high content of information results in a significant increase in classification accuracies for hyperspectral images with respect to optic or multispectral images. In this study, classification with empirical mode decomposition (EMD) and support vector machines (SVM),...
A fusing image classification algorithm is presented, which uses Bag-Of-Features model (BOF) as images' initial semantic features, and subsequently employs Fisher linear discriminative analysis (FLDA) algorithm to get its distribution in a linear optimal subspace as images' final features. Lastly images are classified by K nearest neighbor algorithm. The experimental results indicate that the image...
This paper proposes a multiclass image retrieval method using combined color-frequency-orientation histogram. Shape information, obtained via edge detector and Hough Transform, is also incorporated into the new feature. The feature has shown advantage in both unsupervised and supervised learning on Corel image dataset containing 10 categories of 1000 complex scenes. In unsupervised learning, comparing...
This paper proposes a cascaded classifier framework for better image recognition. The proposed method is based on the confidence values given by the classifiers. By using our proposed topN-Exemplar SVM in the second stage and comparing the confidence values with those from the first stage, the classification results with less confidence are successfully updated. The validity of our algorithm has been...
In recent years, nature inspired remote sensing image classification has become a global research area for acquiring the geo-spatial information from satellite data. The findings of recent studies are showing strong evidence to the fact that various classifiers perform differently when applied to images having different natural terrain features. This paper is an analytical study and a performance...
Image-To-Class distance is first proposed in Naive-Bayes Nearest-Neighbor. NBNN is a feature-based image classifier, and can achieve impressive classification accuracy. However, the performance of NBNN relies heavily on the large number of training samples. If using small number of training samples, the performance will degrade. The goal of this paper is to address this issue. The main contribution...
Remote sensing image classification has been widely applied in many fields such as resource exploration, environmental monitoring and urban planning. Support Vector Machine (SVM) is adopted in our research, to classify two sets of SPOT-5 images of an urban area. In order to achieve high classification accuracies, the kernel function of the SVM classifier is selected beforehand. Furthermore, the kernel...
Various types of orthogonal moments have been widely used for object recognition and classification. This paper presents an effective way of extracting texture features, Bessel Fourier moments, for image retrieval and classification applications. The Bessel Fourier moments are calculated for rotation invariance and perform better in terms of represent global features than orthogonal Fourier-Mellin...
Due to rapid growth of computerized medical imagery, the research area of medical image classification has been very active for the past decade. This paper presents an approach to achieve high recognition rate from classification of medical x-ray images. The methodology is based on local binary pattern as a feature extraction technique and support vector machine (SVM) as a classifier. This classification...
True digital photos and the digital images of scanned photographs have very different properties. The illumination pattern and palette of the two kinds of images are different. Being able to distinguish between them is important, as each of these should be handled during printing with a class-specific pipeline of image transformation algorithms, and misclassification results in detrimental imaging...
The local feature (e.g. SIFT) and Bag of Words (BOW) model play key roles in achieving a state-of-the-art performance for image classification. Although we realize that utilizing extra color information will undoubtedly boost the local feature, there still have not been any research that have carefully focused on how to efficiently transfer this color boosted local feature into a boosted BOW. In this...
Hyperspectral images consist of large number of spectral bands but many of which contain redundant information. Therefore, band selection has been a common practice to reduce the dimensionality of the data space for cutting down the computational cost and alleviating from the Hughes phenomenon. This paper presents a new technique for band selection where a sparse representation of the hyperspectral...
Bag-of-features has become very popular in Image classification. Offline codebook learning has to limit the number of training sample concerned with memory, and it influences classification accuracy to some extent. We propose an online sparse learning algorithm, which utilizes the reconstruction error to update the current codebook. It can capture salient properties of images in real-time. Most of...
Combining global scene classification with object detection has helped in improving the classification accuracy. However, training an object detector requires a large amount of manual annotation. The object detector may also fail when the object is occluded. Meanwhile, the presence of the object is not only indicated by the entire object region but any of its parts or its correlations with other regions...
For enhancing the accuracy and the versatility of automatic image classification, on the basis of the traditional Ant Colony Algorithm, we try to analysis the images from a new angle, and introduce the model of Ant Colony Algorithm, and take the exploratory research. According to the biological characteristic of the ant colony's community intelligence's, this method achieves the classification of...
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