
Since the last decade, Content-Based Image Retrieval was a hot topic research. The computational complexity and the retrieval accuracy are the main problems that CBIR systems have to avoid. To avoid these problems, this paper proposes a new content-based image retrieval method that uses color texture and edge direction feature . Color features are the fundamental characteristics of the content of images. Color feature is one of the most widely used features in low level feature. Texture provides the measures of properties such as smoothness, coarseness, and regularity. The edge of the image is another important feature that represented the content of the image. Using color ,texture and edge direction feature to describe the image and use them for image retrieval is more accurate than using one of them
Color Moment, Local Binary Pattern(LBP), Texture, Edge Histogram, Content-Based Image Retrieval (CBIR)
Color Moment, Local Binary Pattern(LBP), Texture, Edge Histogram, Content-Based Image Retrieval (CBIR)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
