영상검색을 위한 HSV콘의 적응적 다차원 분할

Abstract

The study mainly covers the subject of content based image retrieval (CBIR) using adaptive segmentation of HSV cone. Image segmentation is a low-level task that consists on partitioning the image into homogeneous regions, according to some criteria. It is a crucial step in computer vision systems involving image processing (e.g. content-based image retrieval) where the challenge is to perform an image segmentation with some semantic meaning. Although promising results are presented in many papers, genericity is still not proven. In fact, many of these approaches suffer from a subjective tuning of key parameters. This problem also occurs in many vision systems where segmentation stage is narrowly tuned according to the application domain specities by a human expert in image processing. Some specialized applications for image based query and retrieval are researched and some useful results are produced. Many references have been studied in order to rank various notable features of images, which prove key indices or identification marks of various images. Under the light of most reputable and most remarkable research, the features were rigorously tested and confirmed for their solidity. Simulation was performed using Java and MATLAB.