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.
Hidden Markov models (HMMs) have been widely used in various fields, including image categorization and retrieval. Most of the existing methods train HMMs by low-level features of image blocks; however, the blockbased features can not reflect high-level semantic concepts well. This paper proposes a new method to train HMMs by region-based features, which can be obtained after image segmentation. Our work can be characterized by two key properties: (1) Region-based HMM is adopted to achieve better categorization performance, for the region-based features accord with the human perception better. (2) Multi-layer semantic representation (MSR) is introduced to couple with region-based HMM in a long-term relevance feedback framework for image retrieval. The experimental results demonstrate the effectiveness of our proposal in both aspects of categorization and retrieval.
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