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In this paper we present an eficient two-step approach of using a shape characterization function called centroid-contour distance curve and the object eccentricity (or elongation) for leaf image retrieval. Both the centroid-contour distance curve and the eccentricity of a leaf image are scale, rotation, and translation invariant after proper normalizations. In the frist step, the eccentricity is...
Peer-to-peer networking offers a scalable solution for sharing multimedia data across the network. With a large amount of visual data distributed among different nodes, it is an important but challenging issue to perform content-based retrieval in peer-to-peer networks. While most of the existing methods focus on indexing high dimensional visual features and have limitations of scalability, in this...
The timely and accurate identification of plant species is a persistent challenge as pressure from human activity threatens global flora biodiversity. Most existing research on computer based plant species identification has focused on using leaf contour, signature and spectral analysis techniques alongside textural properties of the leaf lamina. However, these global feature based methods often suffer...
Recently, the Bag-of-Features (BoF) model has emerged as a popular solution to scalable content-based image retrieval (CBIR), due to great success of the Bag-of-Words (BoW) model in textual information processing. While most of the existing algorithms on CBIR in P2P networks focus on indexing high dimensional low level features, we propose to address such an issue by employing the BoF model. However,...
Image annotation, which labels an image with a set of semantic terms so as to bridge the semantic gap between low level features and high level semantics in visual information retrieval, is generally posed as a classification problem. Recently, multi-label classification has been investigated for image annotation since an image presents rich contents and can be associated with multiple concepts (i...
Annotating image regions has been a challenging open issue in many areas such as image content understanding and image retrieval. In this paper, rather than solely rely on visual features of image regions, a novel approach is proposed to improve region annotation by taking concept constraints into account, since high level conceptual information such as image categories can increase the confidence...
Image annotation plays an important role in bridging the semantic gap between low level features and high level semantic contents in image access. In this paper, such a task is tackled by annotating regions which are primitives of a visual scene. We propose a probabilistic model to characterize spatial context for region annotation. Such a model provides a unifying framework integrating both feature...
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