Fuzzy Feature Space Research Articles

ПОЛІПШЕННЯ ПІВТОНОВИХ ЗОБРАЖЕНЬ НА ОСНОВІ АВТОМАТИЗОВАНОЇ ОЦІНКИ ЯСКРАВОСТІ НЕЧІТКИХ ФУНКЦІЙ НАЛЕЖНОСТІ

Зображення, сформовані різними системами, які є результатом стандартних методів дослідження, часто мають якість, недостатню для проведення достовірного аналізу. Вони містять спотворення, обумовлені як системою їх формування, так і методами подання та відображення в системі обробки. Для підвищення достовірності аналізу зображень необхідне поліпшення їх візуальних характеристик з точки зору виявлення об'єктів інтересу для вирішення конкретного завдання. Сучасний підхід до вирішення завдання аналізу зображень через неточність, неповноту вихідних даних та неоднозначність алгоритмів обробки (наприклад, при визначенні класів, регіонів/меж об'єктів) базується на використанні нечітких методів. В роботі розглянуто інформаційні можливості характеристик, синтезованих на основі методу сингулярного розкладання в нечіткому просторі ознак для поліпшення якості напівтонових зображень. Описано існуючі підходи застосування нечітких функцій типу_2 і вплив способу їх формування на одержуваний результат. Запропонований в роботі алгоритм використовує для переходу до нечітких множин типу_2 статистичні характеристики нечітких функцій приналежності типу_1. Запропоновано метод автоматизованого відбору найбільш інформативних нечітких складових, на основі аналізу їх характеристик яскравості, на етапі дефаззифікації із застосуванням сингулярного розкладання. Представлено алгоритм та експериментальні результати на прикладі реального мікроскопічного зображення для різних методів попередньої обробки вихідних даних, які демонструють, що попередня обробка вихідних даних істотно впливаює на чутливість перетворення. Показано, що перехід в нечіткий простір ознак типу_2 та подальше застосування сингулярного перетворення з попереднім відбором найбільш інформативних нечітких функцій приналежності, що інтерпретуються як зображення, на основі автоматизованої оцінки їх яскравості, забезпечує поліпшення візуальних характеристик напівтонових знімків.

Transfer Representation Learning With TSK Fuzzy System

Transfer learning can address the learning tasks of unlabeled data in the target domain by leveraging plenty of labeled data from a different but related source domain. A core issue in transfer learning is to learn a shared feature space where the distributions of the data from the two domains are matched. This learning process can be named as transfer representation learning (TRL). Feature transformation methods are crucial to ensure the success of TRL. The most commonly used feature transformation method in TRL is kernel-based nonlinear mapping to the high-dimensional space, followed by linear dimensionality reduction. But the kernel functions are lack of interpretability, and it is difficult to select kernel functions. To this end, this article proposes a more intuitive and interpretable method, called TRL with TSK-FS (TRL-TSK-FS), by combining TSK fuzzy system (TSK-FS) with transfer learning. Specifically, TRL-TSK-FS realizes TRL from two aspects. On one hand, the data in the source and target domains are transformed into the fuzzy feature space where the distribution distance of the data between the two domains is minimized. On the other hand, discriminant information and geometric properties of the data are preserved by linear discriminant analysis and principal component analysis. A further advantage is that nonlinear transformation is realized in the proposed method by constructing fuzzy mapping with the antecedent part of the TSK-FS instead of kernel functions, which are difficult to be selected. Extensive experiments are conducted on text and image datasets to demonstrate the superiority of the proposed method.

Image modal analysis in art design and image recognition using AI techniques

Traditional art design models are inefficient and difficult to control the modal characteristics of images. Based on the actual needs of artistic design, this paper builds an intelligent model of artistic design image modal analysis based on machine learning technology and image recognition technology, and proposes a feature extraction method based on wavelet transform and fuzzy logic and an art image classification method based on rotating quaternion wavelet transform. Moreover, this paper calculates the activation intensity value corresponding to each fuzzy area in the fuzzy feature space and normalizes these activation intensity values to form the artistic image feature vector. In addition, this paper uses rotating quaternary wavelet transform to decompose the art image, and then calculate the energy and standard deviation of each decomposed sub-band coefficient. Finally, this paper uses support vector machines to recognize artistic images. The experimental research shows that the model constructed in this paper has certain effects.

A fuzzy feature fusion method for auto-segmentation of gliomas with multi-modality diffusion and perfusion magnetic resonance images in radiotherapy

The diffusion and perfusion magnetic resonance (MR) images can provide functional information about tumour and enable more sensitive detection of the tumour extent. We aimed to develop a fuzzy feature fusion method for auto-segmentation of gliomas in radiotherapy planning using multi-parametric functional MR images including apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV). For each functional modality, one histogram-based fuzzy model was created to transform image volume into a fuzzy feature space. Based on the fuzzy fusion result of the three fuzzy feature spaces, regions with high possibility belonging to tumour were generated automatically. The auto-segmentations of tumour in structural MR images were added in final auto-segmented gross tumour volume (GTV). For evaluation, one radiation oncologist delineated GTVs for nine patients with all modalities. Comparisons between manually delineated and auto-segmented GTVs showed that, the mean volume difference was 8.69% (±5.62%); the mean Dice’s similarity coefficient (DSC) was 0.88 (±0.02); the mean sensitivity and specificity of auto-segmentation was 0.87 (±0.04) and 0.98 (±0.01) respectively. High accuracy and efficiency can be achieved with the new method, which shows potential of utilizing functional multi-parametric MR images for target definition in precision radiation treatment planning for patients with gliomas.

No-reference image quality assessment using interval type 2 fuzzy sets

Image quality assessment of distorted or decompressed images without any reference to the original image is challenging from computational point of view. Quality of an image is best judged by human observers without any reference image, and evaluated using subjective measures. The paper aims at designing a generic no-reference image quality assessment (NR-IQA) method by incorporating human visual perception in assigning quality class labels to the images. Using fuzzy logic approach, we consider information theoretic entropies of visually salient regions of images as features and assess quality of the images using linguistic values. The features are transformed into fuzzy feature space by designing an algorithm based on interval type-2 (IT2) fuzzy sets. The algorithm measures uncertainty present in the input–output feature space to predict image quality accurately as close to human observations. We have taken a set of training images belonging to five different pre-assigned quality class labels for calculating foot print of uncertainty (FOU) corresponding to each class. To assess the quality class label of the test images, maximum of T-conorm applied on the lower and upper membership functions of the test images belonging to different classes is calculated. Our proposed image quality metric is compared with other no-reference quality metrics demonstrating more accurate results and compatible with subjective mean opinion score metric.

An effective biomedical image retrieval framework in a fuzzy feature space employing Phase Congruency and GeoSOM

This paper presents a detailed study about a biomedical image retrieval framework by extracting Phase Congruency (PC) features from L*a*b* triplets of images (query, target) and representing them in fuzzy feature space. These features correspond to an edge-corner map of the given image. The resulting map is then processed by Scale Invariant Feature Transform (SIFT) to derive keypoints, that are invariant to affine transformations. The ensuing features were vector quantized to build a codebook of keypoints. The codebook was produced using a Spherical Self-Organizing Map (SOM) built with a geodesic data structure termed as GeoSOM. Then keypoints of the query image are mapped with the codebook and their occurrences are counted to formulate a histogram termed as Phase Congruency-based Bag of Keypoints (PC-BoK). This histogram is generated offline for target images and a similarity measure was performed with the query image to yield the nearest match based on a global fuzzy membership function. Exhaustive experiments of the proposed framework named as BIRS (Biomedical Image Retrieval System) were performed on a diverse medical image collection (NBIA, MESSIDOR, DRIVE). Finally, performance of BIRS demonstrates the advantage of the proposed image representation approach in terms of Precision (P)–Recall (R) parameters. Furthermore relative comparison of the proposed scheme with existing feature descriptors depicts improved P–R values. The proposed feature extraction and representation scheme was also robust against quantization errors.

Image Edge Detection Based on Direction Fuzzy Entropy

To improve the ability of the fuzzy edge detection and anti-noise performance, the paper proposes a new weighted direction fuzzy entropy image edge detection method. The proposed method converts the feature space of image gray to the fuzzy feature space, and then extracts the weighted information measure of the direction structural in the fuzzy entropy feature space. Finally, the proposed method determines the edge pixel by an adaptive threshold after non-maxima suppression. The experiment demonstrates that the proposed method can extract the image edges effectively by means of the fuzzy edge detection.

Motion Frame Analysis and Scene Abstraction: Discrimination Ability of Fuzziness Measures

This article addresses a solution to the problem of scene estimation/abstraction of motion video data in the fuzzy set theoretic framework. Using various fuzzy geometrical and information measures as image features, an algorithm is developed to compute the change of information in each of two successive frames to classify scenes/frames. Frame similarity is measured in terms of weighted distance in fuzzy feature space. This categorization process of raw input visual data can be used to establish structure for correlation. The investigation not only attempts to determine the discrimination ability of the fuzziness measures for classifying scenes, but also enhances the capability of nonlinear, frame-accurate access to video data for applications such as video editing and visual document archival/retrieval systems in multimedia environments.