Incremental evolution of collective network of binary classifier for content-based image classification and retrieval

Author(s):  
Serkan Kiranyaz ◽  
Stefan Uhlmann ◽  
Jenni Pulkkinen ◽  
Turker Ince ◽  
Moncef Gabbouj
Author(s):  
Adrian Carballal ◽  
Luz Castro ◽  
Rebeca Perez ◽  
João Correia

In recent years, there have been attempts to discover the principles that determine the value of aesthetics in the domain of computing. Many and diverse studies have tried in some way to capture these principles through technical characteristics. To this end, helped by the ease of Internet data acquisition, datasets of images have been published which were obtained online at random from websites and photography competitions. To guarantee the validity of a system of aesthetic image classification, one must first guarantee its capacity for generalization. This paper studies how the indiscriminate selection of images can affect the generalization capacity obtained by a binary classifier.


2020 ◽  
Vol 79 (9) ◽  
pp. 781-791
Author(s):  
V. О. Gorokhovatskyi ◽  
I. S. Tvoroshenko ◽  
N. V. Vlasenko

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.


Author(s):  
Sumit Kaur

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 


PIERS Online ◽  
2007 ◽  
Vol 3 (5) ◽  
pp. 625-628
Author(s):  
Jian Yang ◽  
Xiaoli She ◽  
Tao Xiong

Sign in / Sign up

Export Citation Format

Share Document