On the 'Dimensionality Curse' and the 'Self-Similarity Blessing'
IEEE Transactions on Knowledge and Data Engineering
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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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The Bag-of-Features is a popular approach to describe multimedia information by using visual words. The SIFT (Scale Invariant Feature Transform) is one of the most utilized descriptor to model multimedia information in Bag of-Features. The data is described as a set of keypoints and a feature vector is assigned for each of the keypoints. This feature vector is composed of 128 values, which represent the region around each keypoint. In general, some of the detected keypoints are not relevant and can be discarded without losing the local discriminative power. In this paper, we propose a technique to reduce the detected keypoints by SIFT, as well as a technique to reduce the feature vector dimensionality. Experiments were made in order to analyze the performance of the proposed reduction techniques using two different image databases. The results demonstrated that the proposed techniques improve the performance of the image retrieval by reducing up to 50% the feature vector dimensionality of SIFT and at the same time providing a gain of computational time of modeling an image employing Bag-of-Features.