Effect of image partitioning on content-based image retrieval using colour and texture

The current application of content-based image retrieval (CBIR) was urgently needed to process image retrieval in large image databases. Previous research had succeeded in combining feature extraction methods using texture and colour. Combining the two methods of Gray Level Co-Occurrence matrix with Colour moment produces a higher level of precision than that produced when method is applied individually. This research examined more deeply about the effect of image partitioning using the grid partitioning method. The results of precision and recall on the CBIR method would be compared with no partitions, two partitions and three image partitions. The results obtained indicated that the addition of image partitions did not affect the precision value. Thus, it can be concluded that increasing the image partition increases the recall value of the overall CBIR performance.