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Local feature descriptors are the most frequently used feature representation in many Computer Vision problems. In particular, high level semantic information extraction from low-level features in classification and retrieval is also quite successful. Region based approaches to classification and retrieval have become very popular. In this study, popular segmentation methods in the literature are...
Image classification is currently a vital and challenging topic in computer vision. Although it has been achieved many classification algorithms so far, the classification of natural images still remains great difficulties in image processing. In this paper, we propose a semantic linear-time graph kernel for image classification. Each image is represented by a graph and the vertex of each graph corresponds...
Scene-context plays an important role in scene analysis and object recognition. Among various sources of scene-context, we focus on scene-context scale, which means the effective region size of local context to classify an image pixel in a scene. This paper presents semantic segmentation and object recognition using scene-context scale. The scene-context scale can be estimated by the entropy of the...
Automatic image annotation (AIA) plays an important role and attracts much research attention in image understanding and retrieval. Annotation can be posed as classification problems where each annotation keyword is defined as a group of database images labeled with a semantic word. It is shown that, by establishing one-to-one corresponding between image region and semantic keyword is a feasible approach...
Given an image, our proposed model can extract its dominant high-level semantics information through low-level feature extraction and image classification. It contains 3 main parts: image segmentation, feature extraction and classification. To our knowledge, this is the first model that applies Color and Edge Directivity Descriptor (CEDD), a multiple feature extraction algorithm, into the high-level...
This paper provides a method for indoor semantic mapping in 3D environment. For indoor environment constructed by numerous planar surfaces, plane features are extracted and classified to build the main structure of indoor scene. To identify and cognize different objects located in indoor scene, both the position information and the color information are used in object classification. After the background...
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