Endoscopic image analysis in semantic space

Endoscopic image analysis in semantic space

A novel approach to the design of a semantic, low-dimensional, encoding for endoscopic imagery is proposed. This encoding is based on recent advances in scene recognition, where semantic modeling of image content has gained considerable attention over the last decade. While the semantics of scenes are mainly comprised of environmental concepts such as vegetation, mountains or sky, the semantics...

Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models

Image Semantic Analysis and Understanding

Image understanding is widely used in many areas like satellite imaging, robotic technologies, sensory networks, medical and biomedical imaging, intelligent transportation systems, etc. But it is difficult by traditional image processing. Recently semantic analysis has become an active research topic aimed at resolving the gap between low level image features and high level semantics which is a...

Automatic image annotation via label transfer in the semantic space

Automatic image annotation is among the fundamental problems in computer vision and pattern recognition, and it is becoming increasingly important in order to develop algorithms that are able to search and browse large-scale image collections. In this paper, we propose a label propagation framework based on Kernel Canonical Correlation Analysis (KCCA), which builds a latent semantic space where...

Investigating Fuzzy Reasoning in Semantic Image Analysis

Recent advances in semantic image analysis have brought forth generic methodologies to support concept learning at large scale. The attained performance however is highly variable, reflecting effects related to similarities and variations in the visual manifestations of semantically distinct concepts, much as to the limitations issuing from considering semantics solely in the form of perceptual...