Content-based Image Retrieval (CBIR) is retrieving the
desired images from huge collections. The user queries are
becoming very specific and traditional text-based methods
cannot efficiently handle them. CBIR system retrieves the
image via low-level features such as color, texture and shape.
In this work, we propose CBIR system that retrieves images
from a database based on the semantic features of them.
Our methodology divide the query image into 100 regions.
And then, extracts Features Vector from each region and label
each one with the suitable concept like (Sky, Sand, Water,
trunks, foliage, rocks,..., and Grass). The labeling process in
performed semi-automatically using k-means clustering and
KNN classification algorithms. The system has been
evaluated by recall and precision measures and compared to
other recent works. The results of the paper reflects the
efficiency of the system for retrieving images with up to 98%
recognition ratio.