A novel robust image hashing scheme based on quaternion Zernike moments (QZMs) and the scale invariant feature transform (SIFT) is proposed for image authentication. The proposed method can locate tampered region and detect the nature of the modification, including object insertion, removal, replacement, copy-move and cut-to-paste operations. QZMs considered as global features are used for image authentication while SIFT key-point features provide image forgery localization and classification. Proposed approach performance were evaluated on the color images database of UCID and compared with several recent and efficient methods. These experiments show that the proposed scheme provides a short hash length that is robust to most common image content-preserving manipulations like large angle rotations, and allows us to correctly locating forged image regions as well as detecting types of forgery image.
相似文献Cervical cell classification has important clinical significance in cervical cancer screening at early stages. However, there are fewer public cervical cancer smear cell datasets, the weights of each classes’ samples are unbalanced, the image quality is uneven, and the classification research results based on CNN tend to overfit. To solve the above problems, we propose a cervical cell image generation model based on taming transformers (CCG-taming transformers) to provide high-quality cervical cancer datasets with sufficient samples and balanced weights, we improve the encoder structure by introducing SE-block and MultiRes-block to improve the ability to extract information from cervical cancer cells images; we introduce Layer Normlization to standardize the data, which is convenient for the subsequent non-linear processing of the data by the ReLU activation function in feed forward; we also introduce SMOTE-Tomek Links to balance the source data set and the number of samples and weights of the images we use Tokens-to-Token Vision Transformers (T2T-ViT) combing transfer learning to classify the cervical cancer smear cell image dataset to improve the classification performance. Classification experiments using the model proposed in this paper are performed on three public cervical cancer datasets, the classification accuracy in the liquid-based cytology Pap smear dataset (4-class), SIPAKMeD (5-class), and Herlev (7-class) are 98.79%, 99.58%, and 99.88%, respectively. The quality of the images we generated on these three data sets is very close to the source data set, the final averaged inception score (IS), Fréchet inception distance (FID), Recall and Precision are 3.75, 0.71, 0.32 and 0.65 respectively. Our method improves the accuracy of cervical cancer smear cell classification, provides more cervical cell sample images for cervical cancer-related research, and assists gynecologists to judge and diagnose different types of cervical cancer cells and analyze cervical cancer cells at different stages, which are difficult to distinguish. This paper applies the transformer to the generation and recognition of cervical cancer cell images for the first time.
相似文献Automated plant recognition based on leaf images is a challenging task among the researchers from several fields. This task requires distinguishing features derived from leaf images for assigning class label to a leaf image. There are several methods in literature for extracting such distinguishing features. In this paper, we propose a novel automated framework for leaf identification. The proposed framework works in multiple phases i.e. pre-processing, feature extraction, classification using bagging approach. Initially, leaf images are pre-processed using image processing operations such as boundary extraction and cropping. In the feature extraction phase, popular nature inspired optimization algorithms viz. Spider Monkey Optimization (SMO), Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) have been exploited for reducing the dimensionality of features. In the last phase, a leaf image is classified by multiple classifiers and then output of these classifiers is combined using majority voting. The effectiveness of the proposed framework is established based on the experimental results obtained on three datasets i.e. Flavia, Swedish and self-collected leaf images. On all the datasets, it has been observed that the classification accuracy of the proposed method is better than the individual classifiers. Furthermore, the classification accuracy for the proposed approach is comparable to deep learning based method on the Flavia dataset.
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The underwater images suffer from low contrast and color distortion due to variable attenuation of light and nonuniform absorption of red, green and blue components. In this paper, we propose a Retinex-based underwater image enhancement approach. First, we perform underwater image enhancement using the contrast limited adaptive histogram equalization (CLAHE), which limits the noise and enhances the contrast of the dark components of the underwater image at the cost of blurring the visual information. Then, in order to restore the distorted colors, we perform the Retinex-based enhancement of the CLAHE processed image. Next, in order to restore the distorted edges and achieve smoothing of the blurred parts of image, we perform bilateral filtering on the Retinex processed image. In order to utilize the individual strengths of CLAHE, Retinex and bilateral filtering algorithms in a single framework, we determine the suitable parameter values. The qualitative and quantitative performance comparison with some of the existing approaches shows that the proposed approach achieves better enhancement of the underwater images.
相似文献Anomaly detection is an important issue, which has been deeply studied in different research domains and application fields. The dendritic cell algorithm (DCA) is one of the most popular artificial immune system inspired approaches to handle anomaly detection problems. The performance of DCA depends significantly on the parameters used to compute the relationship between input instance and detectors. However, we find that while the DCA’s performance is good in practical applications, it is difficult to analyze due to the empirical based parameters and lacks adaptability. This paper studies how to effectively learn appropriate parameters for deterministic DCA (dDCA) for anomaly detection tasks. In particular, we propose a novel immune optimization based dDCA (IO-dDCA) for anomaly detection. It consists of dDCA classification, T cell (TC) classification, gradient descent optimization and immune nonlinear dynamic optimization. First, the dDCA is regarded as a binary classifier, and the data instances which are labeled as normal will be classified by a T cell inspired classification method, so as to improve the classification performance of dDCA. Then, to improve dDCA’s adaptability, gradient descent is adopted for dDCA parameters’ optimization. Finally, the immune nonlinear model is introduced to adjust learning rate in gradient descent to find the optimal parameters. The theoretical and experimental performance analysis of IO-dDCA show effectiveness of the novel approach through simulations, and the experimental results show that the proposed IO-dDCA has good classification accuracy.
相似文献For almost the past four decades, image classification has gained a lot of attention in the field of pattern recognition due to its application in various fields. Given its importance, several approaches have been proposed up to now. In this paper, we will present a dyadic multi-resolution deep convolutional neural wavelets’ network approach for image classification. This approach consists of performing the classification of one class versus all the other classes of the dataset by the reconstruction of a Deep Convolutional Neural Wavelet Network (DCNWN). This network is based on the Neural Network (NN) architecture, the Fast Wavelet Transform (FWT) and the Adaboost algorithm. It consists, first, of extracting features using the FWT based on the Multi-Resolution Analysis (MRA). These features are used to calculate the inputs of the hidden layer. Second, those inputs are filtered by using the Adaboost algorithm to select the best ones corresponding to each image. Third, we create an AutoEncoder (AE) using wavelet networks of all images. Finally, we apply a pooling for each hidden layer of the wavelet network to obtain a DCNWN that permits the classification of one class and rejects all other classes of the dataset. Classification rates given by our approach show a clear improvement compared to those cited in this article.
相似文献In recent years, image scene classification based on low/high-level features has been considered as one of the most important and challenging problems faced in image processing research. The high-level features based on semantic concepts present a more accurate and closer model to the human perception of the image scene content. This paper presents a new multi-stage approach for image scene classification based on high-level semantic features extracted from image content. In the first stage, the object boundaries and their labels that represent the content are extracted. For this purpose, a combined method of a fully convolutional deep network and a combined network of a two-class SVM-fuzzy and SVR are used. Topic modeling is used to represent the latent relationships between the objects. Hence in the second stage, a new combination of methods consisting of the bag of visual words, and supervised document neural autoregressive distribution estimator is used to extract the latent topics (topic modeling) in the image. Finally, classification based on Bayesian method is performed according to the extracted features of the deep network, objects labels and the latent topics in the image. The proposed method has been evaluated on three datasets: Scene15, UIUC Sports, and MIT-67 Indoor. The experimental results show that the proposed approach achieves average performance improvement of 12%, 11% and 14% in the accuracy of object detection, and 0.5%, 0.6% and 1.8% in the mean average precision criteria of the image scene classification, compared to the previous state-of-the-art methods on these three datasets.
相似文献In the register detection of printing field, a new approach based on Zernike-CNNs is proposed. The edge feature of image is extracted by Zernike moments (ZMs), and a recursive algorithm of ZMs called Kintner method is derived. An improved convolutional neural networks (CNNs) are investigated to improve the accuracy of classification. Based on the classic convolutional neural network (CNN), the improved CNNs adopt parallel CNN to enhance local features, and adopt auxiliary classification part to modify classification layer weights. A printed image is trained with 7?×?400 samples and tested with 7?×?100 samples, and then the method in this paper is compared with other methods. In image processing, Zernike is compared with Sobel method, Laplacian of Gaussian (LoG) method, Smallest Univalue Segment Assimilating Nucleus (SUSAN) method, Finite Impusle Response (FIR) method, Multi-scale Morphological Gradient (MMG) method. In image classification, improved CNNs are compared with classical CNN. The experimental results show that Zernike-CNNs have the best performance, the mean square error (MSE) of the training samples reaches 0.0143, and the detection accuracy of training samples and test samples reached 91.43% and 94.85% respectively. The experiments reveal that Zernike-CNNs are a feasible approach for register detection.
相似文献The Reversible data hiding (RDH) approach can retrieve the original image from the marked image without any distortion. RDH in encrypted images is an approach that hides extra information into the ciphertext using a skill of recovering the actual data losslessly. To guarantee reversibility for addressing the information redundancy drawback, the cover image pixels are copied into two images. This paper presents a high capacity RDH scheme in encrypted images using fuzzy-based encryption. Initially, the texture classification is processed by a convolutional neural network (CNN) to classify the dense and transparent region. It automatically identifies the significant features without any individual supervision. Then, the plain text encryption is activated by the fuzzy group teaching with infinite elliptic curve (FGTIE) method. To overcome the demerit of FCM, the GTA is hybrid with FCM approach and the encryption is processed by the IE method. Next, a new embedding approach is used to enhance the embedding capacity, namely quotient multi-pixel value differencing (QMPVD). In order to obtain the higher PSNR and payload, the multi-pixel differencing is hybrid with the quotient value differencing. Finally, the original data is extracted and recovered with good quality and high capacity. The performances are evaluated using several performance metrics such as PSNR, SSIM, BER, MSE, embedding capacity/payload, sensitivity, specificity, tampering ratio, correlation coefficient, number of pixel change rate and unified average changing intensity. The performance of PSNR and capacity is compared with existing approaches named Encrypted image-based RDH with Paillier cryptosystem (EIRDH-PC), EIRDH with Redundancy Transfer (EIRDH-RT) and EIRDH with pixel value ordering (EIRDH-PVO). The performance is calculated for three groups of images such as the brain, lungs and abdomen. The implementation results show that the introduced model attained better performance compared to existing approaches in terms of PSNR and capacity. Besides, the proposed approach achieved the merits of no pixel expansion, lossless and alternative order recovery.
相似文献Generative Adversarial Networks (GANs) are most popular generative frameworks that have achieved compelling performance. They follow an adversarial approach where two deep models generator and discriminator compete with each other. They have been used for many applications especially for image synthesis because of their capability to generate high quality images. In past few years, different variants of GAN have proposed and they produced high quality results for image generation. This paper conducts an analysis of working and architecture of GAN and its popular variants for image generation in detail. In addition, we summarize and compare these models according to different parameters such as architecture, training method, learning type, benefits and performance metrics. Finally, we apply all these methods on a benchmark MNIST dataset, which contains handwritten digits and compare qualitative and quantitative results. The evaluation is based on quality of generated images, classification accuracy, discriminator loss, generator loss and computational time of these models. The aim of this study is to provide a comprehensive information about GAN and its various models in the field of image synthesis. Our main contribution in this work is critical comparison of popular GAN variants for image generation on MNIST dataset. Moreover, this paper gives insights regarding existing limitations and challenges faced by GAN and discusses associated future research work.
相似文献Extreme learning machine for single-hidden-layer feedforward neural networks has been extensively applied in imbalanced data learning due to its fast learning capability. Ensemble approach can effectively improve the classification performance by combining several weak learners according to a certain rule. In this paper, a novel ensemble approach on weighted extreme learning machine for imbalanced data classification problem is proposed. The weight of each base learner in the ensemble is optimized by differential evolution algorithm. Experimental results on 12 datasets show that the proposed method could achieve more classification performance compared with the simple vote-based ensemble method and non-ensemble method.
相似文献In this paper, we propose a novel hash code generation method based on convolutional neural network (CNN), called the piecewise supervised deep hashing (PSDH) method to directly use a latent layer data and the output layer result of the classification network to generate a two-segment hash code for every input image. The first part of the hash code is the class information hash code, and the second part is the feature message hash code. The method we proposed is a point-wise approach and it is easy to implement and works very well for image retrieval. In particular, it performs excellently in the search of pictures with similar features. The more similar the images are in terms of color and geometric information and so on, the better it will rank above the search results. Compared with the hashing method proposed so far, we keep the whole hashing code search method, and put forward a piecewise hashing code search method. Experiments on three public datasets demonstrate the superior performance of PSDH over several state-of-art methods.
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