A novel method for creating diverse ensembles of image classifiers is proposed. The idea is that, for each base image classifier in the ensemble, a random image transformation is generated and applied to all of the images in the labeled training set. The base classifiers are then learned using features extracted from these randomly transformed versions of the training data, and the result is a ...

In this paper, we propose a two-phase Content-Based Retrieval System for images embedded in the Neutrosophic domain. In this first phase, we extract a set of features to represent the content of each image in the training database. In the second phase, a similarity measurement is used to determine the distance between the image under consideration (query image), and each image in the training d...

Osteoarthritis is a disease found in the world, including in Indonesia. The purpose of this study was to detect the disease Osteoarthritis using Self Organizing mapping (SOM), and to know the procedure of artificial intelligence on the methods of Self Organizing Mapping (SOM). In this system, there are several stages to preserve to detect disease Osteoarthritis using Self Organizing maps is the...

Multispectral remote sensing images have been widely used for automated land use and land cover classification tasks. Often thematic classification is done using single date image, however in many instances a single date image is not informative enough to distinguish between different land cover types. In this paper we show how one can use multiple images, collected at different times of year (...

An important topic in face recognition is to determine the proper level of training—both in training set size and quality—for efficient learning of a face recognition algorithm. Another topic is the effect of time lapse between the target image and the query image on recognition performance. Both topics require a large set of face images where the same subjects have their images acquired repeat...

A class of morphological/rank/linear (MRL)-filters is presented as a general nonlinear tool for image processing. They consist of a linear combination between a morphological/rank filter and a linear filter. A gradient steepest descent method is proposed to optimally design these filters, using the averaged least mean squares (LMS) algorithm. The filter design is viewed as a learning process, a...

We present a fast approximate nearest neighbor algorithm for semantic segmentation. Our algorithm builds a graph over superpixels from an annotated set of training images. Edges in the graph represent approximate nearest neighbors in feature space. At test time we match superpixels from a novel image to the training images by adding the novel image to the graph. A move-making search algorithm a...

Recently, several discriminative learning approaches have been proposed for effective image restoration, achieving convincing trade-off between image quality and computational efficiency. However, these methods require separate training for each restoration task (e.g., denoising, deblurring, demosaicing) and problem condition (e.g., noise level of input images). This makes it time-consuming and...

We propose an end-to-end learning framework for foreground object segmentation. Given a single novel image, our approach produces a pixel-level mask for all “object-like” regions—even for object categories never seen during training. We formulate the task as a structured prediction problem of assigning a foreground/background label to each pixel, implemented using a deep fully convolutional net...

An image classification scheme using Naïve Bayes Classifier is proposed in this paper. The proposed Naive Bayes Classifier-based image classifier can be considered as the maximum a posteriori decision rule. The Naïve Bayes Classifier can produce very accurate classification results with a minimum training time when compared to conventional supervised or unsupervised learning algorithms. Compreh...