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Similar image/shape retrieval has attractedincreasing interests in recent years. A typical strategy of existing retrieval algorithms is to rank the images according to the image-to-image similarities, e.g., the similarities betwee...
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Similar image/shape retrieval has attractedincreasing interests in recent years. A typical strategy of existing retrieval algorithms is to rank the images according to the image-to-image similarities, e.g., the similarities between the query image and the images in the database. This strategy ignores the inherent information of the class that the query image belongs to (we call it query class). To address this issue, rather than using image-to-image similarity, we propose a simple yet effective retrieval method based on exploring the image-to-class similarity. The method uses an iterative framework, where the size of the query class is progressively enlarged according to the previous retrieval results, and the ranked list is generated according to the similarities between the images in the database and the query class. This framework enables us to explore the inherent information of the query class, and hence helps to improve the retrieval accuracy. Experimental results on various datasets demonstrate that our method is able to effectively improve the image and shape retrieval accuracy compared to state-of-the-art methods. (C) 2016 Elsevier B.V. All rights reserved.
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Recently, the proposal of graph convolutional networks (GCN) has successfully implemented into hyperspectral image data representation and analysis. In spite of the great success, there are still several major challenges in hypers...
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Recently, the proposal of graph convolutional networks (GCN) has successfully implemented into hyperspectral image data representation and analysis. In spite of the great success, there are still several major challenges in hyperspectral image classification, including within-class diversity, and between-class similarity, which generally degenerate hyperspectral image classification performance. To address the problems, we propose a discriminative graph convolution networks (DGCN) for hyperspectral image classification. This method introduces the concepts of within-class scatter and between-class scatter, which respectively reflect the global geometric structure and discriminative information of the input space. The experimental results on the hyperspectral data sets show that the proposed method has good classification performance.
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The classification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Although deep learning has shown proven advantages over traditional methods that rely on the handcrafted fe...
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The classification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Although deep learning has shown proven advantages over traditional methods that rely on the handcrafted features, it remains challenging due to the significant intra-class variation and inter-class similarity caused by the diversity of imaging modalities and clinical pathologies. In this paper, we propose a synergic deep learning (SDL) model to address this issue by using multiple deep convolutional neural networks (DCNNs) simultaneously and enabling them to mutually learn from each other. Each pair of DCNNs has their learned image representation concatenated as the input of a synergic network, which has a fully connected structure that predicts whether the pair of input images belong to the same class. Thus, if one DCNN makes a correct classification, a mistake made by the other DCNN leads to a synergic error that serves as an extra force to update the model. This model can be trained end-to-end under the supervision of classification errors from DCNNs and synergic errors from each pair of DCNNs. Our experimental results on the ImageCLEF-2015, lmageCLEF-2016, ISIC-2016, and ISIC-2017 datasets indicate that the proposed SDL model achieves the state-of-the-art performance in these medical image classification tasks. (C) 2019 Elsevier B.V. All rights reserved.
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Non-parametric Nearest-Neighbour (NN) image classification is desired in certain applications, because no intensive learning is required. Naive Bayes Nearest Neighbour (NBNN) and its improved version, Local Naive Bayes Nearest Nei...
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Non-parametric Nearest-Neighbour (NN) image classification is desired in certain applications, because no intensive learning is required. Naive Bayes Nearest Neighbour (NBNN) and its improved version, Local Naive Bayes Nearest Neighbour (Local NBNN), are two impressive classifiers that keep a good balance between algorithm accuracy and complexity. Instead of computing image-to-image (I21) distances, these two algorithms calculate image-to-class (I2C) distances. As a consequence, the local image descriptors are not quantised and the performance of such an image classifier is thereby enhanced. In this paper, by applying the concept of saliency detection to the calculation of I2C distances, we separate each training image from each category into two parts: foreground and background. Then we calculate I2C distances for foreground and background to classify the test image. Afterwards, the scores presented by the foreground and the background are used to correct the responses generated by the original NBNN or Local NBNN. Moreover, it is observed that a prior clustering inside each training category is able to reduce time consumption significantly without sacrificing system performance. By combining the contributions above, our approach is superior to the original NBNN and Local NBNN classifiers in terms of both efficiency and accuracy on three datasets: Pami-09, 15-Scene and Caltech-5. (C) 2015 Elsevier B.V. All rights reserved.
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Image-to-class (I2C) distance is a novel measure for image classification and has successfully handled datasets with large intra-class variances. However, due to the lack of a training phase, the performance of this distance is ea...
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Image-to-class (I2C) distance is a novel measure for image classification and has successfully handled datasets with large intra-class variances. However, due to the lack of a training phase, the performance of this distance is easily affected by irrelevant local features that may hurt the classification accuracy. Besides, the success of this I2C distance relies heavily on the large number of local features in the training set, which requires expensive computation cost for classifying test images. On the other hand, if there are small number of local features in the training set, it may result in poor performance. In this paper, we propose a distance learning method to improve the classification accuracy of this I2C distance as well as two strategies for accelerating its NN search. We first propose a large margin optimization framework to learn the I2C distance function, which is modeled as a weighted combination of the distance from every local feature in an image to its nearest-neighbor (NN) in a candidate class. We learn these weights associated with local features in the training set by constraining the optimization such that the I2C distance from image to its belonging class should be less than that to any other class. We evaluate the proposed method on several publicly available image datasets and show that the performance of I2C distance for classification can significantly be improved by learning a weighted I2C distance function. To improve the computation cost, we also propose two methods based on spatial division and hubness score to accelerate the NN search, which is able to largely reduce the on-line testing time while still preserving or even achieving a better classification accuracy.
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Recent developments in medical image analysis techniques make them essential tools in medical diagnosis. Medical imaging is always involved with different kinds of uncertainties. Managing these uncertainties has motivated extensiv...
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Recent developments in medical image analysis techniques make them essential tools in medical diagnosis. Medical imaging is always involved with different kinds of uncertainties. Managing these uncertainties has motivated extensive research on medical image classification methods, particularly for the past decade. Despite being a powerful classification tool, the sparse representation suffers from the lack of sufficient discrimination and robustness, which are required to manage the uncertainty and noisiness in medical image classification issues. It is tried to overcome this deficiency by introducing a new fuzzy discriminative robust sparse representation classifier, which benefits from the fuzzy terms in its optimization function of the dictionary learning process. In this work, we present a new medical image classification approach, fuzzy discriminative sparse representation (FDSR). The proposed fuzzy terms increase the inter-class representation difference and the intraclass representation similarity. Also, an adaptive fuzzy dictionary learning approach is used to learn dictionary atoms. FDSR is applied on Magnetic Resonance Images (MRI) from three medical image databases. The comprehensive experimental results clearly show that our approach outperforms its series of rival techniques in terms of accuracy, sensitivity, specificity, and convergence speed.
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In this paper, we address the problem of novelty detection whose goal is to recognize instances from unseen classes during testing. Our key idea is to leverage the inconsistency between class similarity and (latent) attribute simi...
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In this paper, we address the problem of novelty detection whose goal is to recognize instances from unseen classes during testing. Our key idea is to leverage the inconsistency between class similarity and (latent) attribute similarity. We are motivated by the observation that a novel class may holistically appear like a certain known class (class-level reference) but often exhibits unique properties similar to others (attribute-level references). That is, the related class-and attribute-level references are often inconsistent for a novel class. A new two-stage Class-Attribute Inconsistency Learning network (CAILNet) is proposed to explore class-attribute inconsistency for novelty detection. Stage one aims to learn both class and attribute features based on the class labels and fake attribute labels, and stage two aims to search for the corresponding references and make fine-grained comparisons for final novelty decision. Empirically we conduct comprehensive experiments on three benchmark datasets, and demonstrate state-of-the-art performance. (c) 2022 Elsevier Ltd. All rights reserved.
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XMage is introduced in this paper as a method for partial similarity searching in image databases. Region-based image retrieval is a method of retrieving partially similar images. It has been proposed as a way to accurately proces...
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XMage is introduced in this paper as a method for partial similarity searching in image databases. Region-based image retrieval is a method of retrieving partially similar images. It has been proposed as a way to accurately process queries in an image database. In region-based image retrieval, region matching is indispensable for computing the partial similarity between two images because the query processing is based upon regions instead of the entire image. A naive method of region matching is a sequential comparison between regions, which causes severe overhead and deteriorates the performance of query processing. In this paper, a new image contents representation, called Condensed extended Histogram (CXHistogram), is presented in conjunction with a well-defined distance function CXSim() on the CX-Histogram. The CXSim() is a new image-to-image similarity measure to compute the partial similarity between two images. It achieves the effect of comparing regions of two images by simply comparing the two images. The CXSim() reduces query space by pruning irrelevant images, and it is used as a filtering function before sequential scanning. Extensive experiments were performed on real image data to evaluate XMage. It provides a significant pruning of irrelevant images with no false dismissals. As a consequence, it achieves up to 5.9-fold speed-up in search over the R~*-tree search followed by sequential scanning.
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Combining the spectral and spatial information, we propose a regularized set-to-set distance metric learning method (RSSDML) for the hyperspectral image (HSI) classification. It first performs a local pixel neighborhood preserving...
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Combining the spectral and spatial information, we propose a regularized set-to-set distance metric learning method (RSSDML) for the hyperspectral image (HSI) classification. It first performs a local pixel neighborhood preserving embedding to reduce the dimensionality and meanwhile to preserve the local similarity structures of HSI, and then puts each target spectral pixel and its spatial neighbors into a set, and measures the distance between different sets to reveal the overall differences of different target spectral pixels. In the computation of the set-to-set distance, a regularization strategy is used to differentiate individual pixels in a pixel set and to improve the set-based metric relations. Exploiting both the correlations between neighboring pixels in a pixel set and the similarities between different pixel sets, the proposed RSSDML dramatically improves traditional point-based and set-based metric learning methods and provides better classification results than some state-of-the-art spatial-spectral classifiers on two benchmark hyperspectral data sets. (C) 2016 Elsevier B.V. All rights reserved.
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This paper presents a novel peer-to-peer protocol to efficiently distribute virtual machine images in a datacenter. A primary idea of it is to improve the performance of peer-to-peer content delivery by employing deduplication to ...
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This paper presents a novel peer-to-peer protocol to efficiently distribute virtual machine images in a datacenter. A primary idea of it is to improve the performance of peer-to-peer content delivery by employing deduplication to take advantage of similarity both among and within VM images in cloud datacenters. The efficacy of the proposed scheme is validated through an evaluation that demonstrates substantial performance gains.
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