21 research outputs found
Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm
In recent days, Artificial Neural Network (ANN) can be applied to a vast
majority of fields including business, medicine, engineering, etc. The most
popular areas where ANN is employed nowadays are pattern and sequence
recognition, novelty detection, character recognition, regression analysis,
speech recognition, image compression, stock market prediction, Electronic
nose, security, loan applications, data processing, robotics, and control. The
benefits associated with its broad applications leads to increasing popularity
of ANN in the era of 21st Century. ANN confers many benefits such as organic
learning, nonlinear data processing, fault tolerance, and self-repairing
compared to other conventional approaches. The primary objective of this paper
is to analyze the influence of the hidden layers of a neural network over the
overall performance of the network. To demonstrate this influence, we applied
neural network with different layers on the MNIST dataset. Also, another goal
is to observe the variations of accuracies of ANN for different numbers of
hidden layers and epochs and to compare and contrast among them.Comment: To be published in the 4th IEEE International Conference on
Electrical Engineering and Information & Communication Technology (iCEEiCT
2018
THE USE OF DATA AUGMENTATION AND EXPLORATORY DATA ANALYSIS IN ENHANCING IMAGE FEATURES ON APPLE LEAF DISEASE DATASET
Apples are an essential commodity produced in Batu City, Malang. In 2017, Batu City, Malang, produced 19.1 tons of apples, while in 2018, Batu City, Malang, produced 15.9 tons of apples. It can be concluded that the decline in the number of apple harvests in Batu City, Malang. With the influence of technology in agriculture, the influence of technology can be used to detect diseases on leaves to overcome the decrease in the number of harvests. With the Image Augmentation method used in this study, the existing dataset can have 6x more features. So that the healthy category, which previously had 516 image features, now has 3096 image features, the scab category, which previously had 592 image features, now has 3552 image features and the rust category, which previously had 622 image features, now has 3732 image features. With a dataset with 3000 image features, the model to be made can have a higher accuracy value. The model can be said to be sturdy/sturdy/good, or the model to be made can carry out the classification process with a good level of accuracy
ReLoc: A Restoration-Assisted Framework for Robust Image Tampering Localization
With the spread of tampered images, locating the tampered regions in digital
images has drawn increasing attention. The existing image tampering
localization methods, however, suffer from severe performance degradation when
the tampered images are subjected to some post-processing, as the tampering
traces would be distorted by the post-processing operations. The poor
robustness against post-processing has become a bottleneck for the practical
applications of image tampering localization techniques. In order to address
this issue, this paper proposes a novel restoration-assisted framework for
image tampering localization (ReLoc). The ReLoc framework mainly consists of an
image restoration module and a tampering localization module. The key idea of
ReLoc is to use the restoration module to recover a high-quality counterpart of
the distorted tampered image, such that the distorted tampering traces can be
re-enhanced, facilitating the tampering localization module to identify the
tampered regions. To achieve this, the restoration module is optimized not only
with the conventional constraints on image visual quality but also with a
forensics-oriented objective function. Furthermore, the restoration module and
the localization module are trained alternately, which can stabilize the
training process and is beneficial for improving the performance. The proposed
framework is evaluated by fighting against JPEG compression, the most commonly
used post-processing. Extensive experimental results show that ReLoc can
significantly improve the robustness against JPEG compression. The restoration
module in a well-trained ReLoc model is transferable. Namely, it is still
effective when being directly deployed with another tampering localization
module.Comment: 12 pages, 5 figure
A Survey on Neural Network Interpretability
Along with the great success of deep neural networks, there is also growing
concern about their black-box nature. The interpretability issue affects
people's trust on deep learning systems. It is also related to many ethical
problems, e.g., algorithmic discrimination. Moreover, interpretability is a
desired property for deep networks to become powerful tools in other research
fields, e.g., drug discovery and genomics. In this survey, we conduct a
comprehensive review of the neural network interpretability research. We first
clarify the definition of interpretability as it has been used in many
different contexts. Then we elaborate on the importance of interpretability and
propose a novel taxonomy organized along three dimensions: type of engagement
(passive vs. active interpretation approaches), the type of explanation, and
the focus (from local to global interpretability). This taxonomy provides a
meaningful 3D view of distribution of papers from the relevant literature as
two of the dimensions are not simply categorical but allow ordinal
subcategories. Finally, we summarize the existing interpretability evaluation
methods and suggest possible research directions inspired by our new taxonomy.Comment: This work has been accepted by IEEE-TETC
Filter-Based Probabilistic Markov Random Field Image Priors: Learning, Evaluation, and Image Analysis
Markov random fields (MRF) based on linear filter responses are one of the most popular forms for modeling image priors due to their rigorous probabilistic interpretations and versatility in various applications. In this dissertation, we propose an application-independent method to quantitatively evaluate MRF image priors using model samples. To this end, we developed an efficient auxiliary-variable Gibbs samplers for a general class of MRFs with flexible potentials. We found that the popular pairwise and high-order MRF priors capture image statistics quite roughly and exhibit poor generative properties. We further developed new learning strategies and obtained high-order MRFs that well capture the statistics of the inbuilt features, thus being real maximum-entropy models, and other important statistical properties of natural images, outlining the capabilities of MRFs. We suggest a multi-modal extension of MRF potentials which not only allows to train more expressive priors, but also helps to reveal more insights of MRF variants, based on which we are able to train compact, fully-convolutional restricted Boltzmann machines (RBM) that can model visual repetitive textures even better than more complex and deep models.
The learned high-order MRFs allow us to develop new methods for various real-world image analysis problems. For denoising of natural images and deconvolution of microscopy images, the MRF priors are employed in a pure generative setting. We propose efficient sampling-based methods to infer Bayesian minimum mean squared error (MMSE) estimates, which substantially outperform maximum a-posteriori (MAP) estimates and can compete with state-of-the-art discriminative methods. For non-rigid registration of live cell nuclei in time-lapse microscopy images, we propose a global optical flow-based method. The statistics of noise in fluorescence microscopy images are studied to derive an adaptive weighting scheme for increasing model robustness. High-order MRFs are also employed to train image filters for extracting important features of cell nuclei and the deformation of nuclei are then estimated in the learned feature spaces. The developed method outperforms previous approaches in terms of both registration accuracy and computational efficiency
Fine Art Pattern Extraction and Recognition
This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)