scholarly journals Towards natural object-based image recoloring

2021 ◽  
Vol 8 (2) ◽  
pp. 317-328
Author(s):  
Meng-Yao Cui ◽  
Zhe Zhu ◽  
Yulu Yang ◽  
Shao-Ping Lu
Keyword(s):  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Natural Object ◽  
Image Matting ◽  
Object Boundary ◽  
Color Selection ◽  
Offline Learning ◽  
Object Based ◽  
Data Driven Approach ◽  
Object Level

AbstractExisting color editing algorithms enable users to edit the colors in an image according to their own aesthetics. Unlike artists who have an accurate grasp of color, ordinary users are inexperienced in color selection and matching, and allowing non-professional users to edit colors arbitrarily may lead to unrealistic editing results. To address this issue, we introduce a palette-based approach for realistic object-level image recoloring. Our data-driven approach consists of an offline learning part that learns the color distributions for different objects in the real world, and an online recoloring part that first recognizes the object category, and then recommends appropriate realistic candidate colors learned in the offline step for that category. We also provide an intuitive user interface for efficient color manipulation. After color selection, image matting is performed to ensure smoothness of the object boundary. Comprehensive evaluation on various color editing examples demonstrates that our approach outperforms existing state-of-the-art color editing algorithms.

scholarly journals A PDTB-styled end-to-end discourse parser

2012 ◽  
Vol 20 (2) ◽  
pp. 151-184 ◽  
Author(s):  
ZIHENG LIN ◽  
HWEE TOU NG ◽  
MIN-YEN KAN
Keyword(s):  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Research Work ◽  
Pipeline Architecture ◽  
Multiple Components ◽  
Current State ◽  
Discourse Relations ◽  
Data Driven Approach ◽  
End To End ◽  
Discourse Connectives

AbstractSince the release of the large discourse-level annotation of the Penn Discourse Treebank (PDTB), research work has been carried out on certain subtasks of this annotation, such as disambiguating discourse connectives and classifying Explicit or Implicit relations. We see a need to construct a full parser on top of these subtasks and propose a way to evaluate the parser. In this work, we have designed and developed an end-to-end discourse parser-to-parse free texts in the PDTB style in a fully data-driven approach. The parser consists of multiple components joined in a sequential pipeline architecture, which includes a connective classifier, argument labeler, explicit classifier, non-explicit classifier, and attribution span labeler. Our trained parser first identifies all discourse and non-discourse relations, locates and labels their arguments, and then classifies the sense of the relation between each pair of arguments. For the identified relations, the parser also determines the attribution spans, if any, associated with them. We introduce novel approaches to locate and label arguments, and to identify attribution spans. We also significantly improve on the current state-of-the-art connective classifier. We propose and present a comprehensive evaluation from both component-wise and error-cascading perspectives, in which we illustrate how each component performs in isolation, as well as how the pipeline performs with errors propagated forward. The parser gives an overall system F1 score of 46.80 percent for partial matching utilizing gold standard parses, and 38.18 percent with full automation.

Persistent memory hash indexes

2021 ◽  
Vol 14 (5) ◽  
pp. 785-798
Author(s):  
Daokun Hu ◽  
Zhiwen Chen ◽  
Jianbing Wu ◽  
Jianhua Sun ◽  
Hao Chen
Keyword(s):  
Future Development ◽  
High Performance ◽  
Performance Metrics ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Hash Tables ◽  
Trade Offs ◽  
Depth Analysis ◽  
Persistent Memory ◽  
Memory Modules

Persistent memory (PM) is increasingly being leveraged to build hash-based indexing structures featuring cheap persistence, high performance, and instant recovery, especially with the recent release of Intel Optane DC Persistent Memory Modules. However, most of them are evaluated on DRAM-based emulators with unreal assumptions, or focus on the evaluation of specific metrics with important properties sidestepped. Thus, it is essential to understand how well the proposed hash indexes perform on real PM and how they differentiate from each other if a wider range of performance metrics are considered. To this end, this paper provides a comprehensive evaluation of persistent hash tables. In particular, we focus on the evaluation of six state-of-the-art hash tables including Level hashing, CCEH, Dash, PCLHT, Clevel, and SOFT, with real PM hardware. Our evaluation was conducted using a unified benchmarking framework and representative workloads. Besides characterizing common performance properties, we also explore how hardware configurations (such as PM bandwidth, CPU instructions, and NUMA) affect the performance of PM-based hash tables. With our in-depth analysis, we identify design trade-offs and good paradigms in prior arts, and suggest desirable optimizations and directions for the future development of PM-based hash tables.

scholarly journals ConnectIt

2020 ◽  
Vol 14 (4) ◽  
pp. 653-667
Author(s):  
Laxman Dhulipala ◽  
Changwan Hong ◽  
Julian Shun
Keyword(s):  
Experimental Evaluation ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Graph Connectivity ◽  
Connected Components ◽  
Sampling Strategies ◽  
Spanning Forest ◽  
Speed Up ◽  
Minimum Spanning Forest ◽  
Edge Sampling

Connected components is a fundamental kernel in graph applications. The fastest existing multicore algorithms for solving graph connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering.

scholarly journals Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 458
Author(s):  
Zakaria El Mrabet ◽  
Niroop Sugunaraj ◽  
Prakash Ranganathan ◽  
Shrirang Abhyankar
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Power Systems ◽  
Real Time ◽  
Fault Location ◽  
State Of The Art ◽  
Economic Consequences ◽  
Support Vector ◽  
Detection Accuracy ◽  
Data Driven Approach

Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.

scholarly journals Incorporating Deep Features into GEOBIA Paradigm for Remote Sensing Imagery Classification: A Patch-Based Approach

2020 ◽  
Vol 12 (18) ◽  
pp. 3007 ◽  
Author(s):  
Bo Liu ◽  
Shihong Du ◽  
Shouji Du ◽  
Xiuyuan Zhang
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Remote Sensing Imagery ◽  
Image Objects ◽  
Deep Feature ◽  
Object Based ◽  
Feature Aggregation ◽  
Art Methods ◽  
Object Features ◽  
Land Cover Maps

The fast and accurate creation of land use/land cover maps from very-high-resolution (VHR) remote sensing imagery is crucial for urban planning and environmental monitoring. Geographic object-based image analysis methods (GEOBIA) provide an effective solution using image objects instead of individual pixels in VHR remote sensing imagery analysis. Simultaneously, convolutional neural networks (CNN) have been widely used in the image processing field because of their powerful feature extraction capabilities. This study presents a patch-based strategy for integrating deep features into GEOBIA for VHR remote sensing imagery classification. To extract deep features from irregular image objects through CNN, a patch-based approach is proposed for representing image objects and learning patch-based deep features, and a deep features aggregation method is proposed for aggregating patch-based deep features into object-based deep features. Finally, both object and deep features are integrated into a GEOBIA paradigm for classifying image objects. We explored the influences of segmentation scales and patch sizes in our method and explored the effectiveness of deep and object features in classification. Moreover, we performed 5-fold stratified cross validations 50 times to explore the uncertainty of our method. Additionally, we explored the importance of deep feature aggregation, and we evaluated our method by comparing it with three state-of-the-art methods in a Beijing dataset and Zurich dataset. The results indicate that smaller segmentation scales were more conducive to VHR remote sensing imagery classification, and it was not appropriate to select too large or too small patches as the patch size should be determined by imagery and its resolution. Moreover, we found that deep features are more effective than object features, while object features still matter for image classification, and deep feature aggregation is a critical step in our method. Finally, our method can achieve the highest overall accuracies compared with the state-of-the-art methods, and the overall accuracies are 91.21% for the Beijing dataset and 99.05% for the Zurich dataset.

scholarly journals Scene Recognition Based on Recurrent Memorized Attention Network

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2038
Author(s):  
Xi Shao ◽  
Xuan Zhang ◽  
Guijin Tang ◽  
Bingkun Bao
Keyword(s):  
Recognition Accuracy ◽  
State Of The Art ◽  
Classification Performance ◽  
Scene Recognition ◽  
Experimental Results ◽  
Scene Classification ◽  
Attention Network ◽  
Attention Model ◽  
Scene Image ◽  
Object Based

We propose a new end-to-end scene recognition framework, called a Recurrent Memorized Attention Network (RMAN) model, which performs object-based scene classification by recurrently locating and memorizing objects in the image. Based on the proposed framework, we introduce a multi-task mechanism that contiguously attends on the different essential objects in a scene image and recurrently performs memory fusion of the features of object focused by an attention model to improve the scene recognition accuracy. The experimental results show that the RMAN model has achieved better classification performance on the constructed dataset and two public scene datasets, surpassing state-of-the-art image scene recognition approaches.

Melanoma and Melanocyte Identification from Hyperspectral Pathology Images Using Object-Based Multiscale Analysis

2018 ◽  
Vol 72 (10) ◽  
pp. 1538-1547 ◽  
Author(s):  
Qian Wang ◽  
Qingli Li ◽  
Mei Zhou ◽  
Li Sun ◽  
Song Qiu ◽  
...  
Keyword(s):  
Multiscale Analysis ◽  
Spatial Information ◽  
Detection Method ◽  
Imaging Techniques ◽  
Imaging Analysis ◽  
Skin Imaging ◽  
Object Based ◽  
Optimal Accuracy ◽  
Pathological Image ◽  
Object Level

Pathological skin imaging analysis is identified as an efficient technique to diagnose melanoma and provide necessary information for treatment. Automatic detection of melanoma and melanocytes in the epidermis area can be a challenging task as a result of the variability of melanocytes and similarity among cytological components. In order to develop a practical and reliable approach to address the issue, this paper proposed a melanoma and melanocyte detection method based on hyperspectral pathology images. Given the abundant and related spectral and spatial information associated with the hyperspectral skin pathological image, an object-based method was first used to construct the image into the object level; then a multiscale descriptor was performed to extract specific features of melanoma and melanocytes. A quantitative evaluation of 100 scenes of hyperspectral pathology images from 49 patients showed the optimal accuracy, sensitivity, and specificity of 94.29%, 95.57%, and 93.15%, respectively. The results can be interpreted that hyperspectral pathology imaging techniques help to detect the melanoma and melanocytes effectively and provide useful information for further segmentation and classification.

iHDT++: improving HDT for SPARQL triple pattern resolution

2020 ◽  
Vol 39 (2) ◽  
pp. 2249-2261
Author(s):  
Antonio Hernández-Illera ◽  
Miguel A. Martínez-Prieto ◽  
Javier D. Fernández ◽  
Antonio Fariña
Keyword(s):  
State Of The Art ◽  
Simple Design ◽  
Performance Improvements ◽  
Semantic Data ◽  
Object Based ◽  
Efficient Access ◽  
Memory Footprint ◽  
Significant Performance ◽  
Pattern Resolution

RDF self-indexes compress the RDF collection and provide efficient access to the data without a previous decompression (via the so-called SPARQL triple patterns). HDT is one of the reference solutions in this scenario, with several applications to lower the barrier of both publication and consumption of Big Semantic Data. However, the simple design of HDT takes a compromise position between compression effectiveness and retrieval speed. In particular, it supports scan and subject-based queries, but it requires additional indexes to resolve predicate and object-based SPARQL triple patterns. A recent variant, HDT++, improves HDT compression ratios, but it does not retain the original HDT retrieval capabilities. In this article, we extend HDT++ with additional indexes to support full SPARQL triple pattern resolution with a lower memory footprint than the original indexed HDT (called HDT-FoQ). Our evaluation shows that the resultant structure, iHDT++ , requires 70 - 85% of the original HDT-FoQ space (and up to 48 - 72% for an HDT Community variant). In addition, iHDT++ shows significant performance improvements (up to one level of magnitude) for most triple pattern queries, being competitive with state-of-the-art RDF self-indexes.

Deep Learning-based Roman-Urdu to Urdu Transliteration

2020 ◽  
pp. 2152001
Author(s):  
Mehreen Alam ◽  
Sibt ul Hussain
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
Research Problem ◽  
Attention Mechanism ◽  
Data Driven ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Source Language ◽  
Data Driven Approach ◽  
Modern Machine

Attention-based encoder-decoder models have superseded conventional techniques due to their unmatched performance on many neural machine translation problems. Usually, the encoders and decoders are two recurrent neural networks where the decoder is directed to focus on relevant parts of the source language using attention mechanism. This data-driven approach leads to generic and scalable solutions with no reliance on manual hand-crafted features. To the best of our knowledge, none of the modern machine translation approaches has been applied to address the research problem of Urdu machine transliteration. Ours is the first attempt to apply the deep neural network-based encoder-decoder using attention mechanism to address the aforementioned problem using Roman-Urdu and Urdu parallel corpus. To this end, we present (i) the first ever Roman-Urdu to Urdu parallel corpus of 1.1 million sentences, (ii) three state of the art encoder-decoder models, and (iii) a detailed empirical analysis of these three models on the Roman-Urdu to Urdu parallel corpus. Overall, attention-based model gives state-of-the-art performance with the benchmark of 70 BLEU score. Our qualitative experimental evaluation shows that our models generate coherent transliterations which are grammatically and logically correct.

scholarly journals A Novel Object-Based Deep Learning Framework for Semantic Segmentation of Very High-Resolution Remote Sensing Data: Comparison with Convolutional and Fully Convolutional Networks

2019 ◽  
Vol 11 (6) ◽  
pp. 684 ◽  
Author(s):  
Maria Papadomanolaki ◽  
Maria Vakalopoulou ◽  
Konstantinos Karantzalos
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Novel Object ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Fully Convolutional Networks ◽  
Object Based ◽  
Deep Networks ◽  
Very High

Deep learning architectures have received much attention in recent years demonstrating state-of-the-art performance in several segmentation, classification and other computer vision tasks. Most of these deep networks are based on either convolutional or fully convolutional architectures. In this paper, we propose a novel object-based deep-learning framework for semantic segmentation in very high-resolution satellite data. In particular, we exploit object-based priors integrated into a fully convolutional neural network by incorporating an anisotropic diffusion data preprocessing step and an additional loss term during the training process. Under this constrained framework, the goal is to enforce pixels that belong to the same object to be classified at the same semantic category. We compared thoroughly the novel object-based framework with the currently dominating convolutional and fully convolutional deep networks. In particular, numerous experiments were conducted on the publicly available ISPRS WGII/4 benchmark datasets, namely Vaihingen and Potsdam, for validation and inter-comparison based on a variety of metrics. Quantitatively, experimental results indicate that, overall, the proposed object-based framework slightly outperformed the current state-of-the-art fully convolutional networks by more than 1% in terms of overall accuracy, while intersection over union results are improved for all semantic categories. Qualitatively, man-made classes with more strict geometry such as buildings were the ones that benefit most from our method, especially along object boundaries, highlighting the great potential of the developed approach.

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