Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving the fisher kernel for large-scale image classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Image classification using super-vector coding of local image descriptors
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
A fast MAP adaptation technique for gmm-supervector-based video semantic indexing systems
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Adapted vocabularies for generic visual categorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
q-Gaussian mixture models for image and video semantic indexing
Journal of Visual Communication and Image Representation
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Gaussian mixture models (GMMs) which extend the bag-of-visual-words (BoW) to a probabilistic framework have been proved to be effective for image and video semantic indexing. Recently, the q-Gaussian distribution, which is derived in the non-extensive statistics, has been shown to be useful for representing patterns in many complex systems in physics such as fractals and cosmology. We propose q-Gaussian mixture models (q-GMMs), which are mixture models of q-Gaussian distributions, for image and video semantic indexing. It has a parameter q to control its tail-heaviness. The long-tailed distributions obtained for q1 are expected to effectively represent complexly correlated data, and hence, to improve robustness against outliers. In our experiments, our proposed method outperformed the BoW method and achieved 49.4% and 10.9% in Mean Average Precision on the PASCAL VOC 2010 dataset and the TRECVID 2010 Semantic Indexing dataset, respectively.