pwc-archive/papers-with-abstracts huggingface dataset:
A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust
Authors: Giuliano L. Manso, Helder Knidel, Renato A. Krohling, Jose A. Ventura
Date: 2019-03-19 00:00:00
Tasks: General Classification, Segmentation

Generally, the identification and classification of plant diseases and/or
pests are performed by an expert . One of the problems facing coffee farmers in
Brazil is crop infestation, particularly by leaf rust Hemileia vastatrix and
leaf miner Leucoptera coffeella. The progression of the diseases and or pests
occurs spatially and temporarily. So, it is very important to automatically
identify the degree of severity. The main goal of this article consists on the
development of a method and its i implementation as an App that allow the
detection of the foliar damages from images of coffee leaf that are captured
using a smartphone, and identify whether it is rust or leaf miner, and in turn
the calculation of its severity degree. The method consists of identifying a
leaf from the image and separates it from the background with the use of a
segmentation algorithm. In the segmentation process, various types of
backgrounds for the image using the HSV and YCbCr color spaces are tested. In
the segmentation of foliar damages, the Otsu algorithm and the iterative
threshold algorithm, in the YCgCr color space, have been used and compared to
k-means. Next, features of the segmented foliar damages are calculated. For the
classification, artificial neural network trained with extreme learning machine
have been used. The results obtained shows the feasibility and effectiveness of
the approach to identify and classify foliar damages, and the automatic
calculation of the severity. The results obtained are very promising according
to experts.
Mutual Linear Regression-based Discrete Hashing
Authors: Xingbo Liu, Xiushan Nie, Yilong Yin
Date: 2019-03-15 00:00:00
Tasks: regression

Label information is widely used in hashing methods because of its
effectiveness of improving the precision. The existing hashing methods always
use two different projections to represent the mutual regression between hash
codes and class labels. In contrast to the existing methods, we propose a novel
learning-based hashing method termed stable supervised discrete hashing with
mutual linear regression (S2DHMLR) in this study, where only one stable
projection is used to describe the linear correlation between hash codes and
corresponding labels. To the best of our knowledge, this strategy has not been
used for hashing previously. In addition, we further use a boosting strategy to
improve the final performance of the proposed method without adding extra
constraints and with little extra expenditure in terms of time and space.
Extensive experiments conducted on three image benchmarks demonstrate the
superior performance of the proposed method.
A Novel Pixel-Averaging Technique for Extracting Training Data from a Single Image, Used in ML-Based Image Enlargement
Authors: Amir Rastar
Date: 2019-03-25 00:00:00
Tasks: BIG-bench Machine Learning

Size of the training dataset is an important factor in the performance of a
machine learning algorithms and tools used in medical image processing are not
exceptions. Machine learning tools normally require a decent amount of training
data before they could efficiently predict a target. For image processing and
computer vision, the number of images determines the validity and reliability
of the training set. Medical images in some cases, suffer from poor quality and
inadequate quantity required for a suitable training set. The proposed
algorithm in this research obviates the need for large or even small image
datasets used in machine learning based image enlargement techniques by
extracting the required data from a single image. The extracted data was then
introduced to a decision tree regressor for upscaling greyscale medical images
at different zoom levels. Results from the algorithm are relatively acceptable
compared to third-party applications and promising for future research. This
technique could be tailored to the requirements of other machine learning tools
and the results may be improved by further tweaking of the tools
hyperparameters.
Value of Temporal Dynamics Information in Driving Scene Segmentation
Authors: Li Ding, Jack Terwilliger, Rini Sherony, Bryan Reimer, Lex Fridman
Date: 2019-03-21 00:00:00
Tasks: Scene Segmentation, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

Semantic scene segmentation has primarily been addressed by forming
representations of single images both with supervised and unsupervised methods.
The problem of semantic segmentation in dynamic scenes has begun to recently
receive attention with video object segmentation approaches. What is not known
is how much extra information the temporal dynamics of the visual scene carries
that is complimentary to the information available in the individual frames of
the video. There is evidence that the human visual system can effectively
perceive the scene from temporal dynamics information of the scene's changing
visual characteristics without relying on the visual characteristics of
individual snapshots themselves. Our work takes steps to explore whether
machine perception can exhibit similar properties by combining appearance-based
representations and temporal dynamics representations in a joint-learning
problem that reveals the contribution of each toward successful dynamic scene
segmentation. Additionally, we provide the MIT Driving Scene Segmentation
dataset, which is a large-scale full driving scene segmentation dataset,
densely annotated for every pixel and every one of 5,000 video frames. This
dataset is intended to help further the exploration of the value of temporal
dynamics information for semantic segmentation in video.
Adversarial camera stickers: A physical camera-based attack on deep learning systems
Authors: Juncheng Li, Frank R. Schmidt, J. Zico Kolter
Date: 2019-03-21 00:00:00

Recent work has documented the susceptibility of deep learning systems to adversarial examples, but most such attacks directly manipulate the digital input to a classifier. Although a smaller line of work considers physical adversarial attacks, in all cases these involve manipulating the object of interest, e.g., putting a physical sticker on an object to misclassify it, or manufacturing an object specifically intended to be misclassified. In this work, we consider an alternative question: is it possible to fool deep classifiers, over all perceived objects of a certain type, by physically manipulating the camera itself? We show that by placing a carefully crafted and mainly-translucent sticker over the lens of a camera, one can create universal perturbations of the observed images that are inconspicuous, yet misclassify target objects as a different (targeted) class. To accomplish this, we propose an iterative procedure for both updating the attack perturbation (to make it adversarial for a given classifier), and the threat model itself (to ensure it is physically realizable). For example, we show that we can achieve physically-realizable attacks that fool ImageNet classifiers in a targeted fashion 49.6% of the time. This presents a new class of physically-realizable threat models to consider in the context of adversarially robust machine learning. Our demo video can be viewed at: https://youtu.be/wUVmL33Fx54
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
Authors: Wieland Brendel, Matthias Bethge
Date: 2019-03-20 00:00:00
Proceeding: ICLR 2019 5

Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has
proven notoriously difficult to understand how they reach their decisions. We
here introduce a high-performance DNN architecture on ImageNet whose decisions
are considerably easier to explain. Our model, a simple variant of the
ResNet-50 architecture called BagNet, classifies an image based on the
occurrences of small local image features without taking into account their
spatial ordering. This strategy is closely related to the bag-of-feature (BoF)
models popular before the onset of deep learning and reaches a surprisingly
high accuracy on ImageNet (87.6% top-5 for 33 x 33 px features and Alexnet
performance for 17 x 17 px features). The constraint on local features makes it
straight-forward to analyse how exactly each part of the image influences the
classification. Furthermore, the BagNets behave similar to state-of-the art
deep neural networks such as VGG-16, ResNet-152 or DenseNet-169 in terms of
feature sensitivity, error distribution and interactions between image parts.
This suggests that the improvements of DNNs over previous bag-of-feature
classifiers in the last few years is mostly achieved by better fine-tuning
rather than by qualitatively different decision strategies.
Part-based approximations for morphological operators using asymmetric auto-encoders
Authors: Bastien Ponchon, Santiago Velasco-Forero, Samy Blusseau, Jesus Angulo, Isabelle Bloch
Date: 2019-03-20 00:00:00
Tasks: Decoder

This paper addresses the issue of building a part-based representation of a
dataset of images. More precisely, we look for a non-negative, sparse
decomposition of the images on a reduced set of atoms, in order to unveil a
morphological and interpretable structure of the data. Additionally, we want
this decomposition to be computed online for any new sample that is not part of
the initial dataset. Therefore, our solution relies on a sparse, non-negative
auto-encoder where the encoder is deep (for accuracy) and the decoder shallow
(for interpretability). This method compares favorably to the state-of-the-art
online methods on two datasets (MNIST and Fashion MNIST), according to
classical metrics and to a new one we introduce, based on the invariance of the
representation to morphological dilation.
3D human action analysis and recognition through GLAC descriptor on 2D motion and static posture images
Authors: Mohammad Farhad Bulbul, Saiful Islam, Hazrat Ali
Date: 2019-03-19 00:00:00
Tasks: Action Analysis

In this paper, we present an approach for identification of actions within
depth action videos. First, we process the video to get motion history images
(MHIs) and static history images (SHIs) corresponding to an action video based
on the use of 3D Motion Trail Model (3DMTM). We then characterize the action
video by extracting the Gradient Local Auto-Correlations (GLAC) features from
the SHIs and the MHIs. The two sets of features i.e., GLAC features from MHIs
and GLAC features from SHIs are concatenated to obtain a representation vector
for action. Finally, we perform the classification on all the action samples by
using the l2-regularized Collaborative Representation Classifier (l2-CRC) to
recognize different human actions in an effective way. We perform evaluation of
the proposed method on three action datasets, MSR-Action3D, DHA and UTD-MHAD.
Through experimental results, we observe that the proposed method performs
superior to other approaches.
Non-rigid 3D shape retrieval based on multi-view metric learning
Authors: Haohao Li, Shengfa Wang, Nannan Li, Zhixun Su, Ximin Liu
Date: 2019-03-20 00:00:00
Tasks: 3D Shape Classification, 3D Shape Retrieval, Diversity, Metric Learning, Retrieval

This study presents a novel multi-view metric learning algorithm, which aims
to improve 3D non-rigid shape retrieval. With the development of non-rigid 3D
shape analysis, there exist many shape descriptors. The intrinsic descriptors
can be explored to construct various intrinsic representations for non-rigid 3D
shape retrieval task. The different intrinsic representations (features) focus
on different geometric properties to describe the same 3D shape, which makes
the representations are related. Therefore, it is possible and necessary to
learn multiple metrics for different representations jointly. We propose an
effective multi-view metric learning algorithm by extending the Marginal Fisher
Analysis (MFA) into the multi-view domain, and exploring Hilbert-Schmidt
Independence Criteria (HSCI) as a diversity term to jointly learning the new
metrics. The different classes can be separated by MFA in our method.
Meanwhile, HSCI is exploited to make the multiple representations to be
consensus. The learned metrics can reduce the redundancy between the multiple
representations, and improve the accuracy of the retrieval results. Experiments
are performed on SHREC'10 benchmarks, and the results show that the proposed
method outperforms the state-of-the-art non-rigid 3D shape retrieval methods.
A Weighted Multi-Criteria Decision Making Approach for Image Captioning
Authors: Hassan Maleki Galandouz, Mohsen Ebrahimi Moghaddam, Mehrnoush Shamsfard
Date: 2019-03-17 00:00:00
Tasks: Decision Making, Image Captioning, Retrieval

Image captioning aims at automatically generating descriptions of an image in
natural language. This is a challenging problem in the field of artificial
intelligence that has recently received significant attention in the computer
vision and natural language processing. Among the existing approaches, visual
retrieval based methods have been proven to be highly effective. These
approaches search for similar images, then build a caption for the query image
based on the captions of the retrieved images. In this study, we present a
method for visual retrieval based image captioning, in which we use a multi
criteria decision making algorithm to effectively combine several criteria with
proportional impact weights to retrieve the most relevant caption for the query
image. The main idea of the proposed approach is to design a mechanism to
retrieve more semantically relevant captions with the query image and then
selecting the most appropriate caption by imitation of the human act based on a
weighted multi-criteria decision making algorithm. Experiments conducted on MS
COCO benchmark dataset have shown that proposed method provides much more
effective results in compare to the state-of-the-art models by using criteria
with proportional impact weights .
Boosted Attention: Leveraging Human Attention for Image Captioning
Authors: Shi Chen, Qi Zhao
Date: 2019-03-18 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Captioning

Visual attention has shown usefulness in image captioning, with the goal of
enabling a caption model to selectively focus on regions of interest. Existing
models typically rely on top-down language information and learn attention
implicitly by optimizing the captioning objectives. While somewhat effective,
the learned top-down attention can fail to focus on correct regions of interest
without direct supervision of attention. Inspired by the human visual system
which is driven by not only the task-specific top-down signals but also the
visual stimuli, we in this work propose to use both types of attention for
image captioning. In particular, we highlight the complementary nature of the
two types of attention and develop a model (Boosted Attention) to integrate
them for image captioning. We validate the proposed approach with
state-of-the-art performance across various evaluation metrics.
Concatenated Feature Pyramid Network for Instance Segmentation
Authors: Yongqing Sun, Pranav Shenoy K P, Jun Shimamura, Atsushi Sagata
Date: 2019-03-16 00:00:00
Tasks: Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

Low level features like edges and textures play an important role in
accurately localizing instances in neural networks. In this paper, we propose
an architecture which improves feature pyramid networks commonly used instance
segmentation networks by incorporating low level features in all layers of the
pyramid in an optimal and efficient way. Specifically, we introduce a new layer
which learns new correlations from feature maps of multiple feature pyramid
levels holistically and enhances the semantic information of the feature
pyramid to improve accuracy. Our architecture is simple to implement in
instance segmentation or object detection frameworks to boost accuracy. Using
this method in Mask RCNN, our model achieves consistent improvement in
precision on COCO Dataset with the computational overhead compared to the
original feature pyramid network.
Deep Demosaicing for Edge Implementation
Authors: Ramchalam Kinattinkara Ramakrishnan, Shangling Jui, Vahid Patrovi Nia
Date: 2019-03-26 00:00:00
Tasks: Demosaicking, Neural Architecture Search

Most digital cameras use sensors coated with a Color Filter Array (CFA) to capture channel components at every pixel location, resulting in a mosaic image that does not contain pixel values in all channels. Current research on reconstructing these missing channels, also known as demosaicing, introduces many artifacts, such as zipper effect and false color. Many deep learning demosaicing techniques outperform other classical techniques in reducing the impact of artifacts. However, most of these models tend to be over-parametrized. Consequently, edge implementation of the state-of-the-art deep learning-based demosaicing algorithms on low-end edge devices is a major challenge. We provide an exhaustive search of deep neural network architectures and obtain a pareto front of Color Peak Signal to Noise Ratio (CPSNR) as the performance criterion versus the number of parameters as the model complexity that beats the state-of-the-art. Architectures on the pareto front can then be used to choose the best architecture for a variety of resource constraints. Simple architecture search methods such as exhaustive search and grid search require some conditions of the loss function to converge to the optimum. We clarify these conditions in a brief theoretical study.
Cross-modal Subspace Learning via Kernel Correlation Maximization and Discriminative Structure Preserving
Authors: Jun Yu, Xiao-Jun Wu
Date: 2019-03-26 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

The measure between heterogeneous data is still an open problem. Many research works have been developed to learn a common subspace where the similarity between different modalities can be calculated directly. However, most of existing works focus on learning a latent subspace but the semantically structural information is not well preserved. Thus, these approaches cannot get desired results. In this paper, we propose a novel framework, termed Cross-modal subspace learning via Kernel correlation maximization and Discriminative structure-preserving (CKD), to solve this problem in two aspects. Firstly, we construct a shared semantic graph to make each modality data preserve the neighbor relationship semantically. Secondly, we introduce the Hilbert-Schmidt Independence Criteria (HSIC) to ensure the consistency between feature-similarity and semantic-similarity of samples. Our model not only considers the inter-modality correlation by maximizing the kernel correlation but also preserves the semantically structural information within each modality. The extensive experiments are performed to evaluate the proposed framework on the three public datasets. The experimental results demonstrated that the proposed CKD is competitive compared with the classic subspace learning methods.
Implementation of Fruits Recognition Classifier using Convolutional Neural Network Algorithm for Observation of Accuracies for Various Hidden Layers
Authors: Shadman Sakib, Zahidun Ashrafi, Md. Abu Bakr Siddique
Date: 2019-04-01 00:00:00
Tasks: General Classification

Fruit recognition using Deep Convolutional Neural Network (CNN) is one of the most promising applications in computer vision. In recent times, deep learning based classifications are making it possible to recognize fruits from images. However, fruit recognition is still a problem for the stacked fruits on weighing scale because of the complexity and similarity. In this paper, a fruit recognition system using CNN is proposed. The proposed method uses deep learning techniques for the classification. We have used Fruits-360 dataset for the evaluation purpose. From the dataset, we have established a dataset which contains 17,823 images from 25 different categories. The images are divided into training and test dataset. Moreover, for the classification accuracies, we have used various combinations of hidden layer and epochs for different cases and made a comparison between them. The overall performance losses of the network for different cases also observed. Finally, we have achieved the best test accuracy of 100% and a training accuracy of 99.79%.
Improved Dynamic Time Warping (DTW) Approach for Online Signature Verification
Authors: Azhar Ahmad Jaini, Ghazali Sulong, Amjad Rehman
Date: 2019-03-26 00:00:00
Tasks: Dynamic Time Warping, Time Series, Time Series Analysis

Online signature verification is the process of verifying time series
signature data which is generally obtained from the tablet-based device. Unlike
offline signature images, the online signature image data consists of points
that are arranged in a sequence of time. The aim of this research is to develop
an improved approach to map the strokes in both test and reference signatures.
Current methods make use of the Dynamic Time Warping (DTW) algorithm and its
variant to segment them before comparing each of its data dimension. This paper
presents a modified DTW algorithm with the proposed Lost Box Recovery Algorithm
aims to improve the mapping performance for online signature verification
CUSUM Filter for Brain Segmentation on DSC Perfusion MR Head Scans with Abnormal Brain Anatomy
Authors: Svitlana Alkhimova
Date: 2019-03-26 00:00:00
Tasks: Anatomy, Brain Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

This paper presents a new approach for relatively accurate brain region of
interest (ROI) detection from dynamic susceptibility contrast (DSC) perfusion
magnetic resonance (MR) images of a human head with abnormal brain anatomy.
Such images produce problems for automatic brain segmentation algorithms, and
as a result, poor perfusion ROI detection affects both quantitative
measurements and visual assessment of perfusion data. In the proposed approach
image segmentation is based on CUSUM filter usage that was adapted to be
applicable to process DSC perfusion MR images. The result of segmentation is a
binary mask of brain ROI that is generated via usage of brain boundary
location. Each point of the boundary between the brain and surrounding tissues
is detected as a change-point by CUSUM filter. Proposed adopted CUSUM filter
operates by accumulating the deviations between the observed and expected
intensities of image points at the time of moving on a trajectory. Motion
trajectory is created by the iterative change of movement direction inside the
background region in order to reach brain region, and vice versa after boundary
crossing. Proposed segmentation approach was evaluated with Dice index
comparing obtained results to the reference standard. Manually marked brain
region pixels (reference standard), as well as visual inspection of detected
with CUSUM filter usage brain ROI, were provided by experienced radiologists.
The results showed that proposed approach is suitable to be used for brain ROI
detection from DSC perfusion MR images of a human head with abnormal brain
anatomy and can, therefore, be applied in the DSC perfusion data analysis.