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The problem of classifying samples for which there is no definite label is a challenging one in which multiple annotators will provide a more certain input for a classifier. Unlike most of active learning scenarios that require identifying which images to be annotated, we explore how many annotations can potentially be used per instance (one annotation per instance is only the initial step) and propose...
In this work we present our first results on the ambitious task of providing region-based querying capabilities to our existing Solar Dynamics Observatory (SDO) content-based image-retrieval (CBIR) system. By taking advantage of precomputed image descriptors, we calculate region-based histogram signatures for our training set of previously identified solar events. With these signatures we then explore...
Microscopic analysis of colon biopsy samples is a common medical practice for identifying colon cancer. However, the process is subjective, and leads to significant inter-observerAntra-observer variability. Further, pathologists have to examine many biopsy samples per day, therefore, factors such as expertise and workload of the histopathologists also affect the diagnosis. These limitations of the...
We propose an efficient framework for combining pixel and object-based approaches for Remote Sensing Image Classification using Support Vector Machines (SVMs) and Dempster-Shafer Theory of Evidence (DSTE). The pixel-based technique employs the multispectral information for assigning a pixel to a class according to the spectral similarities between the classes, and the object-based technique operates...
Young children need their parents' love and care but their parents are not always available to tell them stories. This motivates our proposal of a Smart Teddy Bear, a vision-based story teller, to assist young children in their social-emotional growth. When a young child wants to listen to a story, he or she simply opens any page in that book before a Smart Teddy Bear and the system automatically...
In order to describe the characteristics of medical image more fully in different scales and solve the problem of automatic image category annotation, multi-scale feature based medical image classification is discussed. A set of complementary image features in various scales, including gray-level, texture, shape features and features extracted in the frequency domain is used. An ensemble learning...
The linear coding methods for image classification work by projecting each local descriptor into the codebook, and making a tradeoff between minimizing the projection error and representation sparseness or locality. In this procedure, it is inevitable to lose some discriminative information which may be very important for image classification. In this paper, we alleviate the information loss in the...
The amber gemstones classification system is proposed and described in this paper. The amber data used in experiments are collected by amber art craft industry experts and divided manually into 30 classes. The presented investigations were care out in order to find out most accurate and fast classifier for online amber sorting application. QDA, KNN, RBF, and decision tree classifiers were tested....
Features derived from Grey Level Co-occurrence Matrix (GLCM) and Grey Level Run-Length (GLRL) matrix are widely used for image characterization based on texture analysis. In this paper, we propose the application of suitably selected texture discriminating features for classification of oral cancer lesions in digital camera images into six groups. Backpropagation based Artificial Neural Network (BPANN)...
We consider sublinear test-time algorithms for image categorization when the number of classes is very large. Our method builds upon the label tree approach proposed in [1], which decomposes the label set into a tree structure and classify a test example by traversing the tree. Even though this method achieves logarithmic run-time, its performance is limited by the fact that any errors made in an...
Hyper-Spectral Images (HSI) classification is one of essential problems in hyperspectral image processing and one of the major difficulties in supervised hyperspectral image classification is the limited availability of training data, as it is hard to obtain in real remote sensing scenarios. In this paper we have presented our proposed approach to improve the accuracy of HSI in the situations where...
Local descriptors with Bag-of-Words representation were widely used for image classification. Especially, local descriptors of dense spatial sampling were demonstrated to be able to further improve performances of image classification. However, denser spatial sampling is impractical due to huge computation cost. To handle this issue, we propose a new region-based sampling strategy in this paper. We...
Effective image classification becomes an important issue in content-based image retrieval since it can help to organize the massive amount of digital images and serve for many applications such as object identification, web people search, etc. In this paper, the image classification problem is considered as a Multiple-Instance Learning problem, and Multiple-Instance Decision-Based Neural Networks...
Association rule is one of the most important rules in nature. Each type of object in a remotely sensed image relates to special association rules, thus association rules are important features for image classification, and the mining and rational selection of the effective rules is the key issues for accurate classification. In this paper, an approach that integrates association rules analysis and...
One of the important tasks in analyzing hyperspectral image data is the classification process. Support Vector Machine (SVM) is the most popular and widely used classifier, and its performance is ongoing to be further improved. Recently, methods that exploit both spatial and spectral information are more sufficient, robust, useful, and accurate than those accounting for the spectral signature of pixels...
The aim of our work was to design and implement a software solution, which supports quantitative histological analysis of hematoxilin eozin (HE) stained colon tissue samples, identify tissue structures - nuclei, glands and epithelium - using image processing methods. Furthermore, based on the result of the histological segmentation, it gives a suggestion for the negative or malignant status of the...
A classification method based on minimum error thresholding was proposed for the difficulty in classification of froth images. The optimal thresholds were determined by minimum error thresholding for binarizing the original froth images. The bubble area features of the binary images were extracted and then classified with the support vector machine (SVM). The experimental results show that the proposed...
We propose a new algorithm for classification that merges classification with reject option with classification using contextual information. A reject option is desired in many image-classification applications requiring a robust classifier and when the need for high classification accuracy surpasses the need to classify the entire image. Moreover, our algorithm improves the classifier performance...
In this study, we explore potential improvements to a simple image classification methodology. The methodology analyzed uses K-means clustering and a linear support vector machine (SVM) to classify images using raw pixel data from the individual images. We explore improvement of classification accuracy through feature augmentation, use of alternatives to K-means clustering, and modifications to the...
Deep structure learning is a promising new area of work in the field of machine learning. Previous work in this area has shown impressive performance, but all of it has used connectionist models. We hope to demonstrate that the utility of deep architectures is not restricted to connectionist models. Our approach is to use simple, non-connectionist dimensionality reduction techniques in conjunction...
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