期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2016
卷号：7
期号：3
页码：95-100
语种：English
出版社：Ayushmaan Technologies
摘要：The rapid growth of image data on the internet has spurred the demand for methods and tools for efficient search and retrieval. Content Based Image Retrieval (CBIR) is a technique that uses visual contents such as color, shape, texture, and other image features to retrieve similar images from a large repository against a given query image. This has become an active research area with the advent of the digital media in all most all applications. Although many researchers have been done in the field of image search and retrieval, there are still many challenging problems to be solved. As the semantic gap is considered to be the main issue, recent works have focused on semantic-based image retrieval. Most of the proposed approaches learn image semantics by extracting low-level features from entire image. However, such approaches fail to take into consideration the semantic concepts that occur in the images. In this paper, we focus on the SVM based Classification Model for CBIR Process by combining various image Features.
关键词：Content Based Image Retrieval (CBIR);Texture;Gabor Transform (GT);Feature Extraction;SVM
