◾ Large-Scale Correlation- Based Semantic Classification Using MapReduce

2014 ◽  
pp. 191-212
Keyword(s):  
Large Scale ◽  
Semantic Classification

scholarly journals Self-Supervised Natural Image Reconstruction and Rich Semantic Classification from Brain Activity

2020 ◽  
Author(s):  
Guy Gaziv ◽  
Roman Beliy ◽  
Niv Granot ◽  
Assaf Hoogi ◽  
Francesca Strappini ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Large Scale ◽  
Brain Activity ◽  
Semantic Category ◽  
Natural Images ◽  
Natural Image ◽  
Paired Data ◽  
Semantic Classification ◽  
Network Training ◽  
High Level

AbstractReconstructing natural images and decoding their semantic category from fMRI brain recordings is challenging. Acquiring sufficient pairs (image, fMRI) that span the huge space of natural images is prohibitive. We present a novel self-supervised approach for fMRI-to-image reconstruction and classification that goes well beyond the scarce paired data. By imposing cycle consistency, we train our image reconstruction deep neural network on many “unpaired” data: a plethora of natural images without fMRI recordings (from many novel categories), and fMRI recordings without images. Combining high-level perceptual objectives with self-supervision on unpaired data results in a leap improvement over top existing methods, achieving: (i) Unprecedented image-reconstruction from fMRI of never-before-seen images (evaluated by image metrics and human testing); (ii) Large-scale semantic classification (1000 diverse classes) of categories that are never-before-seen during network training. Such large-scale (1000-way) semantic classification capabilities from fMRI recordings have never been demonstrated before. Finally, we provide evidence for the biological plausibility of our learned model. 1

scholarly journals WEAKLY SUPERVISED SEGMENTATION-AIDED CLASSIFICATION OF URBAN SCENES FROM 3D LIDAR POINT CLOUDS

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 151-157 ◽  
Author(s):  
S. Guinard ◽  
L. Landrieu
Keyword(s):  
Large Scale ◽  
Conditional Random Field ◽  
Level Structure ◽  
Point Clouds ◽  
Semantic Classification ◽  
Urban Scenes ◽  
Weakly Supervised ◽  
High Level ◽  
3D Lidar

We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthropic objects of simple shapes, partionning the scene into geometrically-homogeneous segments which size is determined by the local complexity. This segmentation can be integrated into a conditional random field classifier (CRF) in order to capture the high-level structure of the scene. For each cluster, this allows us to aggregate the noisy predictions of a weakly-supervised classifier to produce a higher confidence data term. We demonstrate the improvement provided by our method over two publicly-available large-scale data sets.

scholarly journals A BENCHMARK FOR LARGE-SCALE HERITAGE POINT CLOUD SEMANTIC SEGMENTATION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1419-1426
Author(s):  
F. Matrone ◽  
A. Lingua ◽  
R. Pierdicca ◽  
E. S. Malinverni ◽  
M. Paolanti ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Learning Approaches ◽  
Semantic Classification ◽  
Digital Heritage ◽  
3D Data ◽  
Digital Twins

Abstract. The lack of benchmarking data for the semantic segmentation of digital heritage scenarios is hampering the development of automatic classification solutions in this field. Heritage 3D data feature complex structures and uncommon classes that prevent the simple deployment of available methods developed in other fields and for other types of data. The semantic classification of heritage 3D data would support the community in better understanding and analysing digital twins, facilitate restoration and conservation work, etc. In this paper, we present the first benchmark with millions of manually labelled 3D points belonging to heritage scenarios, realised to facilitate the development, training, testing and evaluation of machine and deep learning methods and algorithms in the heritage field. The proposed benchmark, available at http://archdataset.polito.it/, comprises datasets and classification results for better comparisons and insights into the strengths and weaknesses of different machine and deep learning approaches for heritage point cloud semantic segmentation, in addition to promoting a form of crowdsourcing to enrich the already annotated database.

scholarly journals Large-Scale ALS Data Semantic Classification Integrating Location-Context-Semantics Cues by Higher-Order CRF

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1700
Author(s):  
Wei Han ◽  
Ruisheng Wang ◽  
Daqing Huang ◽  
Cheng Xu
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Point Clouds ◽  
Higher Order ◽  
Semantic Classification ◽  
Benchmark Datasets ◽  
Media Lab

We designed a location-context-semantics-based conditional random field (LCS-CRF) framework for the semantic classification of airborne laser scanning (ALS) point clouds. For ALS datasets of high spatial resolution but with severe noise pollutions, more contexture and semantics cues, besides location information, can be exploited to surmount the decrease of discrimination of features for classification. This paper mainly focuses on the semantic classification of ALS data using mixed location-context-semantics cues, which are integrated into a higher-order CRF framework by modeling the probabilistic potentials. The location cues modeled by the unary potentials can provide basic information for discriminating the various classes. The pairwise potentials consider the spatial contextual information by establishing the neighboring interactions between points to favor spatial smoothing. The semantics cues are explicitly encoded in the higher-order potentials. The higher-order potential operates at the clusters level with similar geometric and radiometric properties, guaranteeing the classification accuracy based on semantic rules. To demonstrate the performance of our approach, two standard benchmark datasets were utilized. Experiments show that our method achieves superior classification results with an overall accuracy of 83.1% on the Vaihingen Dataset and an overall accuracy of 94.3% on the Graphics and Media Lab (GML) Dataset A compared with other classification algorithms in the literature.

scholarly journals Semantic Data Set Construction from Human Clustering and Spatial Arrangement

2021 ◽  
pp. 1-48
Author(s):  
Olga Majewska ◽  
Diana McCarthy ◽  
Jasper J. F. van den Bosch ◽  
Nikolaus Kriegeskorte ◽  
Ivan Vulić ◽  
...  
Keyword(s):  
Semantic Similarity ◽  
Large Scale ◽  
Evaluation Method ◽  
Representation Learning ◽  
Full Description ◽  
Second Phase ◽  
Learning Models ◽  
Data Set ◽  
Semantic Classification ◽  
Similarity Judgments

Research into representation learning models of lexical semantics usually utilizes some form of intrinsic evaluation to ensure that the learned representations reflect human semantic judgments. Lexical semantic similarity estimation is a widely used evaluation method, but efforts have typically focused on pairwise judgments of words in isolation, or are limited to specific contexts and lexical stimuli. There are limitations with these approaches that either do not provide any context for judgments, and thereby ignore ambiguity, or provide very specific sentential contexts that cannot then be used to generate a larger lexical resource. Furthermore, similarity between more than two items is not considered. We provide a full description and analysis of our recently proposed methodology for large-scale data set construction that produces a semantic classification of a large sample of verbs in the first phase, as well as multiway similarity judgments made within the resultant semantic classes in the second phase. The methodology uses a spatial multi-arrangement approach proposed in the field of cognitive neuroscience for capturing multi-way similarity judgments of visual stimuli. We have adapted this method to handle polysemous linguistic stimuli and much larger samples than previous work.We specifically target verbs, but the method can equally be applied to other parts of speech. We perform cluster analysis on the data from the first phase and demonstrate how this might be useful in the construction of a comprehensive verb resource. We also analyze the semantic information captured by the second phase and discuss the potential of the spatially induced similarity judgments to better reflect human notions of word similarity.We demonstrate how the resultant data set can be used for fine-grained analyses and evaluation of representation learning models on the intrinsic tasks of semantic clustering and semantic similarity. In particular, we find that stronger static word embedding methods still outperform lexical representations emerging from more recent pre-training methods, both on word-level similarity and clustering. Moreover, thanks to the data set’s vast coverage, we are able to compare the benefits of specializing vector representations for a particular type of external knowledge by evaluating FrameNet- and VerbNet-retrofitted models on specific semantic domains such as “Heat” or “Motion.”

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

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