Horror image recognition based on context-aware multi-instance learning

Horror content sharing on the Web is a growing phenomenon that can interfere with our daily life and affect the mental health of those involved. As an important form of expression, horror images have their own characteristics that can evoke extreme emotions. In this paper, we present a novel context-aware multi-instance learning (CMIL) algorithm for horror image recognition. The CMIL algorithm identifies horror images and picks out the regions that cause the sensation of horror in these horror images. It obtains contextual cues among adjacent regions in an image using a random walk on a contextual graph. Borrowing the strength of the Fuzzy Support Vector Machine (FSVM), we define a heuristic optimization procedure based on the FSVM to search for the optimal classifier for the CMIL. To improve the initialization of the CMIL, we propose a novel visual saliency model based on tensor analysis. The average saliency value of each segmented region is set as its initial fuzzy membership in the CMIL. The advantage of the tensor-based visual saliency model is that it not only adaptively selects features, but also dynamically determines fusion weights for saliency value combination from different feature subspaces. The effectiveness of the proposed CMIL model is demonstrated by its use in horror image recognition on two large scale image sets collected from the Internet

Um arcabouço para seleção e fusão de classificadores de padrão

Orientadores: Ricardo da Silva Torres, Anderson RochaTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O crescente aumento de dados visuais, seja pelo uso de inúmeras câmeras de vídeo monitoramento disponíveis ou pela popularização de dispositivos móveis que permitem pessoas criar, editar e compartilhar suas próprias imagens/vídeos, tem contribuído enormemente para a chamada ''big data revolution". Esta grande quantidade de dados visuais dá origem a uma caixa de Pandora de novos problemas de classificação visuais nunca antes imaginados. Tarefas de classificação de imagens e vídeos foram inseridos em diferentes e complexas aplicações e o uso de soluções baseadas em aprendizagem de máquina tornou-se mais popular para diversas aplicações. Entretanto, por outro lado, não existe uma ''bala de prata" que resolva todos os problemas, ou seja, não é possível caracterizar todas as imagens de diferentes domínios com o mesmo método de descrição e nem utilizar o mesmo método de aprendizagem para alcançar bons resultados em qualquer tipo de aplicação. Nesta tese, propomos um arcabouço para seleção e fusão de classificadores. Nosso método busca combinar métodos de caracterização de imagem e aprendizagem por meio de uma abordagem meta-aprendizagem que avalia quais métodos contribuem melhor para solução de um determinado problema. O arcabouço utiliza três diferentes estratégias de seleção de classificadores para apontar o menos correlacionados e eficazes, por meio de análises de medidas de diversidade. Os experimentos mostram que as abordagens propostas produzem resultados comparáveis aos famosos métodos da literatura para diferentes aplicações, utilizando menos classificadores e não sofrendo com problemas que afetam outras técnicas como a maldição da dimensionalidade e normalização. Além disso, a nossa abordagem é capaz de alcançar resultados eficazes de classificação usando conjuntos de treinamento muito reduzidosAbstract: The frequent growth of visual data, either by countless available monitoring video cameras or the popularization of mobile devices that allow each person to create, edit, and share their own images and videos have contributed enormously to the so called ''big-data revolution''. This shear amount of visual data gives rise to a Pandora box of new visual classification problems never imagined before. Image and video classification tasks have been inserted in different and complex applications and the use of machine learning-based solutions has become the most popular approach to several applications. Notwithstanding, there is no silver bullet that solves all the problems, i.e., it is not possible to characterize all images of different domains with the same description method nor is it possible to use the same learning method to achieve good results in any kind of application. In this thesis, we aim at proposing a framework for classifier selection and fusion. Our method seeks to combine image characterization and learning methods by means of a meta-learning approach responsible for assessing which methods contribute more towards the solution of a given problem. The framework uses three different strategies of classifier selection which pinpoints the less correlated, yet effective, classifiers through a series of diversity measure analysis. The experiments show that the proposed approaches yield comparable results to well-known algorithms from the literature on many different applications but using less learning and description methods as well as not incurring in the curse of dimensionality and normalization problems common to some fusion techniques. Furthermore, our approach is able to achieve effective classification results using very reduced training setsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computaçã

Novel Application of Neutrosophic Logic in Classifiers Evaluated under Region-Based Image Categorization System

Neutrosophic logic is a relatively new logic that is a generalization of fuzzy logic. In this dissertation, for the first time, neutrosophic logic is applied to the field of classifiers where a support vector machine (SVM) is adopted as the example to validate the feasibility and effectiveness of neutrosophic logic. The proposed neutrosophic set is integrated into a reformulated SVM, and the performance of the achieved classifier N-SVM is evaluated under an image categorization system. Image categorization is an important yet challenging research topic in computer vision. In this dissertation, images are first segmented by a hierarchical two-stage self organizing map (HSOM), using color and texture features. A novel approach is proposed to select the training samples of HSOM based on homogeneity properties. A diverse density support vector machine (DD-SVM) framework that extends the multiple-instance learning (MIL) technique is then applied to the image categorization problem by viewing an image as a bag of instances corresponding to the regions obtained from the image segmentation. Using the instance prototype, every bag is mapped to a point in the new bag space, and the categorization is transformed to a classification problem. Then, the proposed N-SVM based on the neutrosophic set is used as the classifier in the new bag space. N-SVM treats samples differently according to the weighting function, and it helps reduce the effects of outliers. Experimental results on a COREL dataset of 1000 general purpose images and a Caltech 101 dataset of 9000 images demonstrate the validity and effectiveness of the proposed method

A Review of Thyroid Disorder Detection Using Medical Images

Thyroid is one of the largest endocrine gland. It is a small butterfly shaped gland which is located in the front portion of the neck.It is located just below the Adams apple. Thyroid gland produces hormones that help the body to control metabolism. There are various thyroid disorders.It includes Hyperthyroidism, Hypothyroidism, goiter and thyroid nodules (benign/malignant). is most commonly. Various modalities that are used to detect and classify abnormalities of the thyroid gland are Ultrasoundimaging,(ComputerTomography(CT), Magnetic Resonance Imaging(MRI). Computer Aided Diagnosis (CAD) help radiologists and doctors to increase the diagnosis accuracy, reduce biopsy ratio and save their time and effort. Thyroid medical images are utilized for the diagnosis process

FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification

This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory consumption, and ability to stretch assumptions and time complexity. Attaining a fast computational model providing fuzzy logic and supervised learning is one of the main challenges in the machine learning. In this research paper, we present FSL-BM algorithm as an efficient solution of supervised learning with fuzzy logic processing using binary meta-feature representation using Hamming Distance and Hash function to relax assumptions. While many studies focused on reducing time complexity and increasing accuracy during the last decade, the novel contribution of this proposed solution comes through integration of Hamming Distance, Hash function, binary meta-features, binary classification to provide real time supervised method. Hash Tables (HT) component gives a fast access to existing indices; and therefore, the generation of new indices in a constant time complexity, which supersedes existing fuzzy supervised algorithms with better or comparable results. To summarize, the main contribution of this technique for real-time Fuzzy Supervised Learning is to represent hypothesis through binary input as meta-feature space and creating the Fuzzy Supervised Hash table to train and validate model.Comment: FICC201

An automatic system for classification of breast cancer lesions in ultrasound images

Breast cancer is the most common of all cancers and second most deadly cancer in women in the developed countries. Mammography and ultrasound imaging are the standard techniques used in cancer screening. Mammography is widely used as the primary tool for cancer screening, however it is invasive technique due to radiation used. Ultrasound seems to be good at picking up many cancers missed by mammography. In addition, ultrasound is non-invasive as no radiation is used, portable and versatile. However, ultrasound images have usually poor quality because of multiplicative speckle noise that results in artifacts. Because of noise segmentation of suspected areas in ultrasound images is a challenging task that remains an open problem despite many years of research. In this research, a new method for automatic detection of suspected breast cancer lesions using ultrasound is proposed. In this fully automated method, new de-noising and segmentation techniques are introduced and high accuracy classifier using combination of morphological and textural features is used. We use a combination of fuzzy logic and compounding to denoise ultrasound images and reduce shadows. We introduced a new method to identify the seed points and then use region growing method to perform segmentation. For preliminary classification we use three classifiers (ANN, AdaBoost, FSVM) and then we use a majority voting to get the final result. We demonstrate that our automated system performs better than the other state-of-the-art systems. On our database containing ultrasound images for 80 patients we reached accuracy of 98.75% versus ABUS method with 88.75% accuracy and Hybrid Filtering method with 92.50% accuracy. Future work would involve a larger dataset of ultrasound images and we will extend our system to handle colour ultrasound images. We will also study the impact of larger number of texture and morphological features as well as weighting scheme on performance of our classifier. We will also develop an automated method to identify the "wall thickness" of a mass in breast ultrasound images. Presently the wall thickness is extracted manually with the help of a physician

An investigation of the breast cancer classification using various machine learning techniques

It is an extremely cumbersome process to predict a disease based on the visual diagnosis of cell type with precision or accuracy, especially when multiple features are associated. Cancer is one such example where the phenomenon is very complex and also multiple features of cell types are involved. Breast cancer is a disease mostly affects female population and the number of affected people is highest among all cancer types in India. In the present investigation, various pattern recognition techniques were used for the classification of breast cancer using cell image processing. Under these pattern recognition techniques, cell image segmentation, texture based image feature extraction and subsequent classification of breast cancer cells was successfully performed. When four different machine learning techniques: Kth nearest neighbor (KNN), Artificial Neural Network ( ANN), Support Vector Machine (SVM) and Least Square Support Vector Machine (LS-SVM) was used to classify 81 cell images, it was observed from the results that the LS-SVM with both Radial Basis Function (RBF) and linear kernel classifiers demonstrated the highest classification rate of 95.3488% among four other classifiers while SVM with linear kernel resulted a classification rate of 93.02% which was close to LSSVM classifier. Thus, it was demonstrated that the LS-SVM classifier showed accuracy higher than other classifiers reported so far. Moreover, our classifier can classify the disease in a short period of time using only cell images unlike other approaches reported so far

Brain Tumor Detection and Classification of MR Images Using SVM Algorithm

Brain tumor is a mass of tissue and it occurs an abnormal growth of cells, then it form within the brain. To identifying tumor detection and classification using brain MRI image. There are several algorithms are developed for brain tumor detection and classifications in the field of medical image processing.In this undertaking we have proposed a crossover calculation for recognition cerebrum tumor in Magnetic Resonance pictures utilizing measurable highlights and Support Vector Machine (SVM) classifier. The proposed procedure comprises of four phases specifically, Noise decrease, Feature extraction, Feature decrease and Classification. In the first stage anisotropic filter is applied for noise reduction and to make the image suitable for extracting features. In the second stage, acquires the surface highlights identified with MRI pictures. In the third stage, the highlights of attractive reverberation pictures have been decreased utilizing Principles Component Analysis (PCA) to the most fundamental highlights. At the last stage, the Supervisor classifier based SVM has been utilized to group subjects as ordinary and strange mind MR pictures

A study on image segmentation techniques used in color detection

to humans, an image is a meaningful arrangement of regions and objects, whereas to computers, an image is merely a random collection of pixels. This work exploits some of the techniques based on the extraction of the color of an image in the real-time environment. Image segmentation is an intense research area in Computer Vision, however, enhancements or results still on to produce accurate segmentation results for images. Relating with other surveys that compare multiple techniques, this paper takes the advantage to select of the most used technique(s), Our study may be helpful for Augmented Reality environment, object detection and tracking as well as other real -time technologies. Interested reader will obtain know ledge on various categories and types of research challenges In the image-based segmentation within the scope of colored images environments