In order to organize,manage,browse and retrieve image database effectively,a novel image retrieval algorithm combining global statistical feature and local binary bitmap feature was proposed.The mean value and standard deviation of every image were calculated to obtain the global statistical feature.Then,according to the idea of block truncation code,every image was divided into 4×4 image blocks without overlapping,and mean value of every image block was calculated also.If mean value was larger than that of the whole image,the image block was set as "1",otherwise,the image block as "0",so the image bitmap feature was obtained.Finally,the normalized features were integrated and retrieved by the best similar matching function.Experimental results indicate that the retrieval performance using combined global and local features is prior to that using single feature;compared with other similar schemes,the algorithm is robust,and the mean retrieval precision reaches 63% in the first 100 images,which is 4% higher than that of other two schemes mentioned in the paper