Wan-Lei Zhao

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This paper proposes a pooling strategy for local descriptors to produce a vector representation that is orientation-invariant yet implicitly incorporates the relative angles between features measured by their dominant orientation. This pooling is associated with a similarity metric that ensures that all the features have undergone a comparable rotation.(More)
This paper studies the role and performance of local invariant features arisen from interest points in describing and sketching semantic concepts. Both the local description and spatial location of interest points are exploited, separately and jointly, for concept-based retrieval. In concept description, a visual dictionary is generated with each keyframe(More)
One of the most successful method to link all similar images within a large collection is min-Hash, which is a way to significantly speed-up the comparison of images when the underlying image representation is bag-of-words. However, the quantization step of min-Hash introduces important information loss. In this paper, we propose a generalization of(More)
Colorand texture are two important features in content-based image retrieval. It has been shown that using the combination of both could provide better performance. In this paper, a K-means based histogram (KBH) using the combination of color and texture features for image retrieval is proposed. Multiresolution feature vectors representing color and texture(More)
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