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Visual Features Selection

Giuseppe Amato, Fabrizio Falchi, Claudio Gennaro
2013 Italian Information Retrieval Workshop  
The state-of-the-art algorithms for large visual content recognition and content based similarity search today use the "Bag of Features" (BoF) or "Bag of Words" (BoW) approach.  ...  The BoF approach [Sivic and Zisserman, 2003] quantizes local features extracted from images representing them with the closest local feature chosen from a fixed visual vocabulary of local features (visual  ...  A vocabulary of words is selected among all the local features using the k-means algorithm. 3. The Random or Scale reduction techniques are performed (if requested). 4.  ... 
dblp:conf/iir/AmatoFG13 fatcat:pjwdmgiapfepnj7fu6nnkl3onm

Selecting relevant visual features for speechreading

V. Estellers, M. Gurban, J.P. Thiran
2009 2009 16th IEEE International Conference on Image Processing (ICIP)  
A quantitative measure of relevance is proposed for the task of constructing visual feature sets which are at the same time relevant and compact.  ...  This is justified by the fact that the performance of audio speech recognition can be improved by augmenting the audio features with visual ones, especially when there is noise in the audio channel.  ...  Our contribution is a method of selecting visual features and thus reducing the dimensionality of the visual feature vector for audio-visual speech recognition.  ... 
doi:10.1109/icip.2009.5414563 dblp:conf/icip/EstellersGT09 fatcat:2f6cohgdhbazvkgf6qf5azwrmy

Data Visualization with Simultaneous Feature Selection

Dharmesh M. Maniyar, Ian T. Nabney
2006 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology  
Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches.  ...  Until now there has been no method of deciding feature saliency while training a data visualization model.  ...  We propose a GTM-based data visualization with simultaneous feature selection (GTM-FS) approach which not only provides a better visualization by modeling irrelevant features ("noise") using a separate  ... 
doi:10.1109/cibcb.2006.330985 dblp:conf/cibcb/ManiyarN06 fatcat:dxknnbw52ne5dj25xl2sibapya

Visual SLAM with Network Uncertainty Informed Feature Selection [article]

Pranav Ganti, Steven L. Waslander
2018 arXiv   pre-print
We present SIVO (Semantically Informed Visual Odometry and Mapping), a novel information-theoretic feature selection method for visual SLAM which incorporates machine learning and neural network uncertainty  ...  into the feature selection pipeline.  ...  FEATURE SELECTION CRITERIA We now present our feature selection methodology for long-term visual SLAM.  ... 
arXiv:1811.11946v1 fatcat:mswz3mskzzgt7oks6txyldhg2i

Interpretable Selection and Visualization of Features and Interactions Using Bayesian Forests [article]

Viktoriya Krakovna, Jiong Du, Jun S. Liu
2016 arXiv   pre-print
Our method performs competitively on classification and feature selection benchmarks in low and high dimensions, and includes a visualization tool that provides insight into relevant features and interactions  ...  We present a novel method, Selective Bayesian Forest Classifier, that strikes a balance between predictive power and interpretability by simultaneously performing classification, feature selection, feature  ...  Conclusion Selective Bayesian Forest Classifier is an integrated tool for supervised classification, feature selection, interaction detection and visualization.  ... 
arXiv:1506.02371v4 fatcat:p37acfqfajafdifu76ozqsjatq

Network mechanisms of dynamic feature selection for flexible visual categorizations [article]

Y. Duan
2022 arXiv   pre-print
Current theories and models assume that the visual hierarchy reduces its high-dimensional input, by flexibly selecting the stimulus features that support categorization behavior.  ...  We show where, when and how brain networks select categorization features.  ...  Conclusions We studied the pervasive mechanisms of dynamic feature selection for flexible visual categorizations.  ... 
arXiv:2205.04393v2 fatcat:3m3meuq5ynd5nhwoi75i42klwe

Feature and Region Selection for Visual Learning

Ji Zhao, Liantao Wang, Ricardo Cabral, Fernando De la Torre
2016 IEEE Transactions on Image Processing  
To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model.  ...  This allows for an intermediate visualization of the features and regions that are important for visual learning.  ...  To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model.  ... 
doi:10.1109/tip.2016.2514503 pmid:26742135 fatcat:6v7zgdldzveeblnd5zs6bgsupm

Implicit Attentional Selection of Bound Visual Features

David Melcher, Thomas V. Papathomas, Zoltán Vidnyánszky
2005 Neuron  
selection are not isolated features but are matically bound throughout the visual field. bound clusters of spatiotemporally colocalized features.  ...  However, there is a line and Zoltán Vidnyánszky 4,5, * of experimental results on "feature-based" attentional 1 Department of Psychology selection (Corbetta et al., 1991; Motter, 1994; Treue and Oxford  ...  In fact, this is exactly roneous cross-feature attentional selection of visual what we found in an experiment similar to experiment features at the higher stages of visual processing and 3, with a stronger  ... 
doi:10.1016/j.neuron.2005.04.023 pmid:15924859 fatcat:jlsbaguepra25dvs4dwxy5c244

Salient Feature Selection for Visual Concept Learning [chapter]

Feng Xu, Lei Zhang, Yu-Jin Zhang, Wei-Ying Ma
2005 Lecture Notes in Computer Science  
The experimental results on Corel image database demonstrate that the proposed salient feature selection approach is very effective in image classification and visual concept learning.  ...  To choose representative features for an image category and meanwhile reduce noisy features, a three-step salient feature selection strategy is proposed.  ...  The salient feature selection strategy is effective in generating visual keywords to describe image categories.  ... 
doi:10.1007/11581772_54 fatcat:daoctrf255flfdrvcwe4yxh7om

Guided Feature Selection for Deep Visual Odometry [article]

Fei Xue, Qiuyuan Wang, Xin Wang, Wei Dong, Junqiu Wang, Hongbin Zha
2018 arXiv   pre-print
We present a novel end-to-end visual odometry architecture with guided feature selection based on deep convolutional recurrent neural networks.  ...  Different from current monocular visual odometry methods, our approach is established on the intuition that features contribute discriminately to different motion patterns.  ...  Guided Feature Selection for Deep Visual Odometry In this section, we introduce our framework ( Fig. 1 ) in detail. First, the model encodes RGB images to high-level features in § 3.1.  ... 
arXiv:1811.09935v1 fatcat:dt2yhqilhfbkvgetg6egyarrki

Automatic selection of visual features and classifiers

Alejandro Jaimes, Shih-Fu Chang, Minerva M. Yeung, Boon-Lock Yeo, Charles A. Bouman
1999 Storage and Retrieval for Media Databases 2000  
In this paper, we propose a dynamic approach to feature and classifier selection. In our approach, based on performance, visual features and classifiers are selected automatically.  ...  These results demonstrate the importance, feasibility, and usefulness of dynamic feature/classifier selection for classification of visual information, and the performance benefits of using multiple learning  ...  We present a new approach, based on performance, in which visual features and classifiers are selected automatically in building Visual Object Detectors.  ... 
doi:10.1117/12.373566 dblp:conf/spieSR/JaimesC00 fatcat:dkbkoaopafeufmewey3hzff3n4

Visualizing Time Series Consistency for Feature Selection

Lena Cibulski, Thorsten May, Bernhard Preim, Jürgen Bernard, Jörn Kohlhammer
2019 Journal of WSCG  
ABSTRACT Feature selection is an effective technique to reduce dimensionality, for example when the condition of a system is to be understood from multivariate observations.  ...  The selection of variables often involves a priori assumptions about underlying phenomena. To avoid the associated uncertainty, we aim at a selection criterion that only considers the observations.  ...  DISCUSSION The most important benefit of our visual feature selection technique is its generality.  ... 
doi:10.24132/jwscg.2019.27.2.2 fatcat:ldhrsjifibhj7jd4ckzqu344hm

Feature-specific effects of selective visual attention

Andrew F. Rossi, Michael A. Paradiso
1995 Vision Research  
Selective attention Spatial frequency Orientation Detection  ...  Because the detectability of the peripheral grating is different when different features of the central stimuli are discriminated, we suggest that the effects on sensitivity are due to feature-specific  ...  Accounts of the findings not involving attention The original goal of our experiments was to determine whether one can selectively attend to particular features of visual stimuli and, if so, whether this  ... 
doi:10.1016/0042-6989(94)00156-g pmid:7900301 fatcat:36a4c44t2rfbdhnowtgcqx52gu

Feature Selection Convolutional Neural Networks for Visual Tracking [article]

Zhiyan Cui, Na Lu
2018 arXiv   pre-print
In this paper, a feature selection visual tracking algorithm combining CNN based MDNet(Multi-Domain Network) and RoIAlign was developed.  ...  So valid feature maps are selected by mutual information and others are abandoned which can reduce the complexity and computation of the network and do not affect the precision.  ...  CONCLUSIONS A feature selection convolution neural network for visual tracking was proposed.  ... 
arXiv:1811.08564v1 fatcat:qbn2ij56dvdvtahydr22cajbui

Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry [article]

Lintong Zhang, David Wisth, Marco Camurri, Maurice Fallon
2021 arXiv   pre-print
Second, we select a fixed budget of tracked features across the cameras to reduce back-end optimization time.  ...  We present a multi-camera visual-inertial odometry system based on factor graph optimization which estimates motion by using all cameras simultaneously while retaining a fixed overall feature budget.  ...  . • A submatrix feature selection (SFS) scheme that selects the best landmarks for optimization with a fixed feature budget.  ... 
arXiv:2109.05975v2 fatcat:tf6n323ebnalblojn3pkdtsyka
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