KEYWORDS: 3D modeling, Distance measurement, 3D image processing, Databases, Data modeling, Principal component analysis, RGB color model, Object recognition, Cameras, 3D vision
Light field is a novel image-based representation of 3D object, in which each 3D object is described by a group of images captured from many viewpoints. It is irrelevant to the complexity of the 3D scenario or objects. Due to this advantage, we propose a 3D object retrieval framework based on light field. An effective distance measure through subspace analysis of light field data is defined, and our method makes use of the structural information hidden in the images of light field. To obtain a more reasonable distance measure, the distance in low dimensional spaces is supplemented. Additionally, our algorithm can handle the problem of arbitrary camera numbers and positions when capturing the light field. In our experiment, a standard 3D object database is adopted, and our proposed distance measure shows better performance than the "LFD" in 3D object retrieval and recognition.
This paper proposes an effcient multi-ranking algorithm for content based image retrieval based on view selection.
The algorithm treats multiple sets of features as views, and selects effective ones from them for ranking tasks
using a data-driven training algorithm. A set of views with different weights are obtained through interaction
between all the views by both self-enforcement and co-reduction. The final sets of views are quite small and
reasonable, yet the effectiveness of original feature sets is preserved. This algorithm provides the potential of
scaling up to large data sets without losing retrieval accuracy. Our experimental retrieval results on real world
image sets demonstrate the effectiveness and effciency of our framework.
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