Deep Adaptive Networks for Visual Data Classification
نویسندگان
چکیده
This paper proposes a classifier called deep adaptive networks (DAN) based on deep belief networks (DBN) for visual data classification. First, we construct a directed deep belief nets by using a set of Restricted Boltzmann Machines (RBM) and a Gaussian RBM via greedy and layerwise unsupervised learning. Then, we refine the parameter space of the deep architecture to adapt the classification requirement by using global gradient-descent based supervised learning. An exponential loss function is utilized to maximize the separability of different classes. Moreover, we apply DAN to visual data classification task and observe an important fact that the learning ability of deep architecture is seriously underrated in real-world applications, especially when there are not enough labeled data. Experiments conducted on standard datasets of different types and different scales demonstrate that the proposed classifier outperforms the representative classification techniques and deep learning methods.
منابع مشابه
An adaptive estimation method to predict thermal comfort indices man using car classification neural deep belief
Human thermal comfort and discomfort of many experimental and theoretical indices are calculated using the input data the indicator of climatic elements are such as wind speed, temperature, humidity, solar radiation, etc. The daily data of temperature، wind speed، relative humidity، and cloudiness between the years 1382-1392 were used. In the First step، Tmrt parameter was calculated in the Ray...
متن کاملProvide a Deep Convolutional Neural Network Optimized with Morphological Filters to Map Trees in Urban Environments Using Aerial Imagery
Today, we cannot ignore the role of trees in the quality of human life, so that the earth is inconceivable for humans without the presence of trees. In addition to their natural role, urban trees are also very important in terms of visual beauty. Aerial imagery using unmanned platforms with very high spatial resolution is available today. Convolutional neural networks based deep learning method...
متن کاملAdaptive Neuro-Fuzzy Inference System application for hydrothermal alteration mapping using ASTER data
The main problem associated with the traditional approach to image classification for the mapping of hydrothermal alteration is that materials not associated with hydrothermal alteration may be erroneously classified as hydrothermally altered due to the similar spectral properties of altered and unaltered minerals. The major objective of this paper is to investigate the potential of a neuro-fuz...
متن کاملCystoscopy Image Classication Using Deep Convolutional Neural Networks
In the past three decades, the use of smart methods in medical diagnostic systems has attractedthe attention of many researchers. However, no smart activity has been provided in the eld ofmedical image processing for diagnosis of bladder cancer through cystoscopy images despite the highprevalence in the world. In this paper, two well-known convolutional neural networks (CNNs) ...
متن کاملPorosity classification from thin sections using image analysis and neural networks including shallow and deep learning in Jahrum formation
The porosity within a reservoir rock is a basic parameter for the reservoir characterization. The present paper introduces two intelligent models for identification of the porosity types using image analysis. For this aim, firstly, thirteen geometrical parameters of pores of each image were extracted using the image analysis techniques. The extracted features and their corresponding pore types ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Journal of Multimedia
دوره 9 شماره
صفحات -
تاریخ انتشار 2014