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Histopathology image classification can provide automated support towards cancer diagnosis. In this paper, we present a transfer learning-based approach for histopathology image classification. We first represent the image feature by Fisher Vector (FV) encoding of local features that are extracted using the Convolutional Neural Network (CNN) model pretrained on ImageNet. Next, to better transfer the...
In image classification and retrieval, the semantic gap is the major challenge. It characterizes the difference between human perception of a concept and how it can be represented using machine level language. Bag of visual words is a well-known efficient method for image representation, however it showed some limitations. The loss of information during the vector quantization process is one of these...
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...
In image classification, the most powerful statistical learning approaches are based on the Bag-of-Words paradigm. In this article, we propose an extension of this formalism. Considering the Bag-of-Features, dictionary coding and pooling steps, we propose to focus on the pooling step. Instead of using the classical sum or max pooling strategies, we introduced a density function-based pooling strategy...
The following topics are dealt with : hidden Markov model; support vector machines; microarray sample classification; automated knowledge engineering; medical image edge enhancement; recurrent fuzzy multilayer perceptron; self organizing maps; data mining; business intelligence tool; context ontology driven relevant search; Web search result optimization; image compression analysis; natural feature...
A blind color image steganalyzer is proposed, in which the features are extracted from Contourlet domain. Statistical features of Contourlet coefficients and cooccurrence metrics of subband images are used as features. For evaluating the proposed steganalysis method, some popular steganography methods such as OutGuess, JPHS, Model-based and Jsteg are used with payloads of 10% to 25%. To reduce the...
As compared with text spam, the image spam is a variant which is invented to escape from traditional text-based spam classification and filtering. Various approaches to image spam filtering have been proposed with respective advantages and drawbacks in terms of time cost and efficiency. In this paper, we propose a new approach based on Base64 encoding of image files and n-gram technique for feature...
The double compression of JPEG images is one of the important evidences of image tampering. The paper proposes a novel passive double compressed JPEG image detection algorithm using the moment features of the modes based DCT histogram's characteristic function. Support vector machine is used as the classifier. Experimental results demonstrate that the proposed algorithm significantly increases the...
Propose a New Steganalysis Method for BPCS which base on Markov model. According to BPCS steganography algorithm destroy the continuity of images, divide the least significant bit plane of images to sub-blocks. Then calculate the complexity of the various blocks to form Markov matrix, and scan the matrix to construct the Markov model, extracting features to classification test. In the experiment use...
This paper proposes a steganalysis scheme using the difference histogram and image calibration. The message embedding makes the correlation with the neighboring pixels weaken and the random changes of the pixel values generate the block effects among the pixels. The proposed method crops a suspicious image by 1 pixel in a row or column direction and compares the difference histograms. Two distance...
In scene categorization, one single histogram based on the sole universal codebook is used to characterize an image in most state-of-the-art scene categorization methods, which is lack of enough discriminative ability to separate the images among different categories and results in low classification accuracy. In order to solve the problem, in this paper, we propose a novel scene categorization approach...
This paper presents a new method for satellite image classification. Specifically, we make two main contributions: (1) we introduce the sparse coding method for high-resolution satellite image classification; (2) we effectively combine a set of diverse and complementary features-SIFT, Color Histogram and Gabor to further improve the performance. A two-stage linear SVM classifier is designed for this...
A new face recognition method based on Mahalanobis distance and Support Vector Machine (SVM) using skin color information is proposed. According to face skin color distribution in YCbCr color space,The Mahalanobis distance map of the image is obtained and use Independent Component Analysis (ICA) to extract features and establish Eigenfaces space. A novel SVM method is proposed based on loop-symmetrical...
As image spam becomes widespread and does a lot of harm, it is more important to filter such spam effectively for now. In this paper, We propose a feature extraction scheme that focus on low-level features (metadata and visual features) of image, which can making classification rapid. They are effective because of not rely on extracting text and analyzing the content of email. a one-class SVM classifier...
In this paper, a novel fuzzy support vector machine based image watermarking scheme is proposed.Since the application of support vector machine in the process of watermarking technology is only a simple classification of the image. However,the fuzzy support vector machines by selecting the appropriate degree of membership to reflect the different importance of the different sample points. In this...
This paper reviews the current soft computing (SC) techniques employed in image steganography as well as proposes a new hybrid approach of these SC techniques to exploit their complementary strengths. Four main SC techniques in image steganography - neural network (NN), genetic algorithm (GA), support vector machines (SVM) and fuzzy logic (FL) are assessed based on the three main measurements of steganography...
In this paper, a blind steganalytic scheme is proposed to effectively detect JPEG steganography. Nine statistical models are constructed from the DCT and decompressed spatial domain for a JPEG image. By calculating the histogram characteristic function (HCF) and the center of mass (COM), we measure the energy distribution of each model as one part of our feature set. Moreover, for each model constructed...
In this paper, a new image steganalysis method was proposed based on image gradient energy and entropy features, together with Fraid's proposed wavelet subband coefficients and higher-order statistics of linear prediction error features. We get 74-dimensional features extraction from images. The support vector machines (SVM) is used to class the images. Experimental results show the method can improve...
The aim of this paper is to properly classify various stego images of JPEG to their own stegnographic methods (current steganographic methods, such as F5, OutGuess, Steghide, JPhide and Jsteg). Although some Multiclass Detection methods had been previously published by the authors, they all had various limitations and disadvantages. First, models of some detect methods are too complicated, and their...
Jsteg and F5 are two typical steganography methods of JPEG images and have been used widely. To distinguish F5 stego images and Jsteg stego images, a classification algorithm based on sensitive features and SVM classifier is presented, where the sensitive features are extracted from the subband coefficients of those stego-images and the subband coefficients are obtained by wavelet packet decomposition...
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