10,195 research outputs found
Bag-of-Features Image Indexing and Classification in Microsoft SQL Server Relational Database
This paper presents a novel relational database architecture aimed to visual
objects classification and retrieval. The framework is based on the
bag-of-features image representation model combined with the Support Vector
Machine classification and is integrated in a Microsoft SQL Server database.Comment: 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF),
Gdynia, Poland, 24-26 June 201
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Computer vision (CV) is a big and important field
in artificial intelligence covering a wide range of applications.
Image analysis is a major task in CV aiming to extract, analyse
and understand the visual content of images. However, imagerelated
tasks are very challenging due to many factors, e.g., high
variations across images, high dimensionality, domain expertise
requirement, and image distortions. Evolutionary computation
(EC) approaches have been widely used for image analysis with
significant achievement. However, there is no comprehensive
survey of existing EC approaches to image analysis. To fill
this gap, this paper provides a comprehensive survey covering
all essential EC approaches to important image analysis tasks
including edge detection, image segmentation, image feature
analysis, image classification, object detection, and others. This
survey aims to provide a better understanding of evolutionary
computer vision (ECV) by discussing the contributions of different
approaches and exploring how and why EC is used for
CV and image analysis. The applications, challenges, issues, and
trends associated to this research field are also discussed and
summarised to provide further guidelines and opportunities for
future research
Deep Learning Concepts for Evolutionary Art
A deep convolutional neural network (CNN) trained on millions of images forms a very high-level abstract overview of any given target image. Our primary goal is to use this high-level content information of a given target image to guide the automatic evolution of images. We use genetic programming (GP) to evolve procedural textures. We incorporate a pre-trained deep CNN model into the fitness. We are not performing any training, but rather, we pass a target image through the pre-trained deep CNN and use its the high-level representation as the fitness guide for evolved images. We develop a preprocessing strategy called Mean Minimum Matrix Strategy (MMMS) which reduces the dimensions and identifies the most relevant high-level activation maps. The technique using reduced activation matrices for a fitness shows promising results. GP is able to guide the evolution of textures such that they have shared characteristics with the target image. We also experiment with the fully connected “classifier” layers of the deep CNN. The evolved images are able to achieve high confidence scores from the deep CNN module for some tested target images. Finally, we implement our own shallow convolutional neural network with a fixed set of filters. Experiments show that the basic CNN had limited effectiveness, likely due to the lack of training. In conclusion, the research shows the potential for using deep learning concepts in evolutionary art. As deep CNN models become better understood, they will be able to be used more effectively for evolutionary art
A Convolutional Neural Network for the Automatic Diagnosis of Collagen VI related Muscular Dystrophies
The development of machine learning systems for the diagnosis of rare
diseases is challenging mainly due the lack of data to study them. Despite this
challenge, this paper proposes a system for the Computer Aided Diagnosis (CAD)
of low-prevalence, congenital muscular dystrophies from confocal microscopy
images. The proposed CAD system relies on a Convolutional Neural Network (CNN)
which performs an independent classification for non-overlapping patches tiling
the input image, and generates an overall decision summarizing the individual
decisions for the patches on the query image. This decision scheme points to
the possibly problematic areas in the input images and provides a global
quantitative evaluation of the state of the patients, which is fundamental for
diagnosis and to monitor the efficiency of therapies.Comment: Submitted for review to Expert Systems With Application
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