The nature of statistical learning theory
The nature of statistical learning theory
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection with Embedded Confidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Geometric Context from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Vectorized image segmentation via trixel agglomeration
Pattern Recognition
Retrieval of objects in video by similarity based on graph matching
Pattern Recognition Letters
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Multi-Class Segmentation with Relative Location Prior
International Journal of Computer Vision
International Journal of Computer Vision
A Statistical Approach to Material Classification Using Image Patch Exemplars
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifier fusion for SVM-based multimedia semantic indexing
ECIR'07 Proceedings of the 29th European conference on IR research
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study
IEEE Transactions on Multimedia
Semantic Image Segmentation and Object Labeling
IEEE Transactions on Circuits and Systems for Video Technology
Retrieval of multiple instances of objects in videos
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Content based image retrieval using bag-of-regions
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Multimedia Tools and Applications
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In this paper we study the problem of the detection of semantic objects from known categories in images. Unlike existing techniques which operate at the pixel or at a patch level for recognition, we propose to rely on the categorization of image segments. Recent work has highlighted that image segments provide a sound support for visual object class recognition. In this work, we use image segments as primitives to extract robust features and train detection models for a predefined set of categories. Several segmentation algorithms are benchmarked and their performances for segment recognition are compared. We then propose two methods for enhancing the segments classification, one based on the fusion of the classification results obtained with the different segmentations, the other one based on the optimization of the global labelling by correcting local ambiguities between neighbor segments. We use as a benchmark the Microsoft MSRC-21 image database and show that our method competes with the current state-of-the-art.