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A learning approach to semantic image analysis

G. Th. Papadopoulos, P. Panagi, S. Dasiopoulou
2006 Proceedings of the 2nd international conference on Mobile multimedia communications - MobiMedia '06  
In this paper, a learning approach coupling Support Vector Machines (SVMs) and a Genetic Algorithm (GA) is presented for knowledge-assisted semantic image analysis in specific domains.  ...  Experiments with images of the beach vacation domain demonstrate the performance of the proposed approach.  ...  In this paper, a semantic image analysis approach is proposed that combines two types of learning algorithms, namely SVMs and GAs, with explicitly defined knowledge in the form of an ontology that specifies  ... 
doi:10.1145/1374296.1374327 dblp:conf/mobimedia/PapadopoulosPD06 fatcat:faanoeprznd6bmb6d3ufgdlov4

A Statistical Learning Approach to Spatial Context Exploitation for Semantic Image Analysis

Georgios Th. Papadopoulos, Vasileios Mezaris, Ioannis Kompatsiaris, Michael G. Strintzis
2010 2010 20th International Conference on Pattern Recognition  
In this paper, a statistical learning approach to spatial context exploitation for semantic image analysis is presented.  ...  relations after performing an initial classification of image regions to semantic concepts using solely visual information.  ...  In this paper, a statistical learning approach to spatial context exploitation for semantic image analysis is presented.  ... 
doi:10.1109/icpr.2010.768 dblp:conf/icpr/PapadopoulosMKS10 fatcat:ab3gz55yzjdpxmylkyjfevu4ma

Semantic-Aware Depth Super-Resolution in Outdoor Scenes [article]

Miaomiao Liu, Mathieu Salzmann, Xuming He
2016 arXiv   pre-print
In particular, we design a co-sparse analysis model that learns filters from joint intensity, depth and semantic information.  ...  To this end, we first propose to exploit semantic information to better constrain the super-resolution process.  ...  In particular, we adopt the analysis model framework of [28] and introduce an approach to building a joint prior on image, depth and semantic patches.  ... 
arXiv:1605.09546v1 fatcat:gyj6lnjfpvavjisbomihxnh2by

Vocab SIG: A cognitive semantic approach to L2 learning of phrasal verbs

Brian Strong
2013 The Language Teacher  
It consisted of a semantic analysis along with basic pictures showing the direction of a trajectory in relation to a landmark.  ...  Based on the initial findings, it appears a semantic analysis approach is an effective teaching method that should be used to help learners overcome the confusion experienced when using phrasal verbs.  ...  In a number of cognitive linguistics studies, a semantic analysis approach has contributed to participants learning multiword units.  ... 
doi:10.37546/jalttlt37.5-9 fatcat:n7b4nqk45bcphghggp4extedle

Information Mining from Multimedia Databases

Ling Guan, Horace HS Ip, Paul H Lewis, Hau San Wong, Paisarn Muneesawang
2006 EURASIP Journal on Advances in Signal Processing  
Liu et al. propose a new approach for performing semantic analysis and annotation of basketball video.  ...  Zhang and Chen describe a new approach to extracting objects from video sequences which is based on spatio-temporal independent component analysis and multiscale analysis.  ...  Supervised learning is first applied to train image classifiers based on a small subset of labeled images.  ... 
doi:10.1155/asp/2006/49073 fatcat:louxnv5c5bggrdeyvjw6qavi4m

RCA: Ride Comfort-Aware Visual Navigation via Self-Supervised Learning [article]

Xinjie Yao, Ji Zhang, Jean Oh
2022 arXiv   pre-print
We develop a self-supervised learning framework to predict traversability costmap from first-person-view images by leveraging vehicle states as training signals.  ...  We propose to model ride comfort explicitly in traversability analysis using proprioceptive sensing.  ...  Here, we propose a self-supervised learning approach for the terrain traversability analysis module by exploiting a robot's vehicle state as an additional input to the learning algorithm in addition to  ... 
arXiv:2207.14460v1 fatcat:ajlkf7y3hveaxp266zoxv4ibhe

Making use of Semantic Concept Detection for Modelling Human Preferences in Visual Summarization

Stevan Rudinac, Marcel Worring
2014 Proceedings of the 2014 International ACM Workshop on Crowdsourcing for Multimedia - CrowdMM '14  
Informed by the outcomes of this analysis, we show that the distribution of semantic concepts can be successfully utilized for learning to rank the images based on their likelihood of inclusion in the  ...  summary by a human, and that it can be easily combined with other features related to image content, context, aesthetic appeal and sentiment.  ...  EXPERIMENTAL RESULTS To quantify the contribution of image semantics analysis to the overall improvement in automatic image selection, we compare the performance of the approach presented in Section 4  ... 
doi:10.1145/2660114.2660127 dblp:conf/mm/RudinacW14 fatcat:4bottdkniffilfdpk32llkv5zq

A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

Malambo, Popescu, Ku, Rooney, Zhou, Moore
2019 Remote Sensing  
This study developed and applied an image analysis approach based on a SegNet deep learning semantic segmentation model to estimate sorghum panicles counts, which are critical phenotypic data in sorghum  ...  Analysis approaches based on deep learning models are currently the most promising and show unparalleled performance in analyzing large image datasets.  ...  Special thanks go out to the personnel in the AgriLife Corporate Relations Office who have made significant contributions to various aspects of this large multidisciplinary research activity at Texas A  ... 
doi:10.3390/rs11242939 fatcat:i4qgcmdzevewhekpuk3l4yizve

Learning clinically useful information from images: Past, present and future

Daniel Rueckert, Ben Glocker, Bernhard Kainz
2016 Medical Image Analysis  
This has led to major improvements in all stages of the medical imaging pipeline, from acquisition and reconstruction to analysis and interpretation.  ...  , semantic and intelligent medical imaging.  ...  Acknowledgements The authors would like to thank all current and previous members of the BioMedIA group who are the main contributors to the works discussed in this article.  ... 
doi:10.1016/ pmid:27344105 fatcat:tc6evfswgzaqfikazhnfsl4wsu

Introduction to the special issue on learning semantics

Antoine Bordes, Léon Bottou, Ronan Collobert, Dan Roth, Jason Weston, Luke Zettlemoyer
2013 Machine Learning  
A growing number of efforts to develop machine learning approaches for semantic analysis now aim to find (in an automated way) these interpretations (Miller et al.  ...  A key ambition of AI is to render computers able to evolve and interact with the real world.  ...  We wish to sincerely thank all the authors for submitting their work to this special issue.  ... 
doi:10.1007/s10994-013-5381-4 fatcat:7i5ubznmabewldc5asxj3xqfru

Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks [chapter]

Thomas Schlegl, Sebastian M. Waldstein, Wolf-Dieter Vogl, Ursula Schmidt-Erfurth, Georg Langs
2015 Lecture Notes in Computer Science  
In this paper we propose to use a semantic representation of clinical reports as a learning target that is predicted from imaging data by a convolutional neural network.  ...  Weakly supervised learning approaches can link volume-level labels to image content but suffer from the typical label distributions in medical imaging data where only a small part consists of clinically  ...  The algorithm has to learn a mapping from image location to semantic location information encoded in a semantic target vector. The benefits of this algorithm are two-fold.  ... 
doi:10.1007/978-3-319-19992-4_34 fatcat:f7akmhypuzgjjael77donync7e

A Comparative Study on Semantic Segmentation Algorithms for Autonomous Driving Vehicles

Shivam Chhabra
2022 International Journal for Research in Applied Science and Engineering Technology  
Abstract: Semantic segmentation refers to the process of classifying each pixel in an image for better understanding and analysis of image.  ...  In this research paper we compare the currently proved popular choices of models for semantic segmentation with respect to autonomous vehicles on different parameters to create an in-depth analysis on  ...  Before Deep learning Networks, machine learning algorithms faced a major challenge of extracting features from an image, but the continuously growing field of deep learning never stumbled upon having a  ... 
doi:10.22214/ijraset.2022.44511 fatcat:fyyo2thzevazjbepfc4344tldu

Semantic indexing and computational aesthetics

Miriam Redi
2013 Proceedings of the 3rd ACM conference on International conference on multimedia retrieval - ICMR '13  
Computational Aesthetics provides a set of techniques to automatically assign a beauty degree to a given image.  ...  Semantic Indexing is about automatically identifying content in natural images, namely recognizing objects and scenes.  ...  We chose to model and predict the image interestingness using a SI framework, based on learning techniques over discriminative visual (semantic and aesthetic) features.  ... 
doi:10.1145/2461466.2461532 dblp:conf/mir/Redi13 fatcat:5tu5dqztkjcrrfgseyxcba63ve

Learning to rank images using semantic and aesthetic labels

Naila Murray, Luca Marchesotti, Florent Perronnin
2012 Procedings of the British Machine Vision Conference 2012  
In particular, we compare two families of approaches: while the first one attempts to learn a single ranker which takes into account both semantic and aesthetic information, the second one learns separate  ...  We use large-margin classifiers and rankers to learn statistical models capable of ordering images based on the aesthetic and semantic information.  ...  In particular, we improve state-of-the-art approaches that attempt to learn aesthetic and semantic information jointly.  ... 
doi:10.5244/c.26.110 dblp:conf/bmvc/MurrayMP12 fatcat:g726nxy7nfc45djizsovzjgxcu

Real-time image annotation by manifold-based biased Fisher discriminant analysis

Rongrong Ji, Hongxun Yao, Jicheng Wang, Xiaoshuai Sun, Xianming Liu, William A. Pearlman, John W. Woods, Ligang Lu
2008 Visual Communications and Image Processing 2008  
Automatic Linguistic Annotation is a promising solution to bridge the semantic gap in content-based image retrieval.  ...  This paper presents a novel Manifold-based Biased Fisher Discriminant Analysis (MBFDA) algorithm to address these two issues by transductive semantic learning and keyword filtering.  ...  The authors would also like to thank the anonymous reviewers for their valuable comments and suggestions.  ... 
doi:10.1117/12.767024 fatcat:z5ceyjjj4jabhpkrdddjkxk6v4
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