Invariant Descriptors for 3D Object Recognition and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Texture features for content-based retrieval
Principles of visual information retrieval
Pattern Recognition Letters
Wavelet Based Texture Classification
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Real-time computerized annotation of pictures
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
MyPlaces: detecting important settings in a visual diary
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Performance evaluation of relevance feedback methods
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Interest points based on maximization of distinctiveness
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
The University of Aamsterdam's concept detection system at ImageCLEF 2009
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
RetrievalLab: a programming tool for content based retrieval
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
The leiden augmented reality system (LARS)
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
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Visual concept detection in images has been a challenging task for many years. The recently proposed MIRFLICKR-25000 dataset has set the standards even higher as the wide variety of images and annotations require new techniques to tackle the visual concept detection problem. We propose the use of the recently introduced MOD salient points for subimage visual concept detection. These points are located at regions within an image that are distinctive with respect to the features that are selected for subimage classification. We also introduce the notion of Minimum Explanation Complexity (MEC), where the complexity of classifiers is reduced to a simpler but equally effective form whenever possible. Our experiments on the MIRFLICKR-25000 dataset show that MOD based concept detectors outperform SIFT based features. We also show that a neural network classifier based on the MEC notion, outperforms a standard SVM classifier.