Direct histogram to histogram matching in content-based image retrieval is not proficient due to its large number of bins. The total number of bins of an original histogram represents the large dimensional feature descriptor which requires high computational overhead during the retrieval process. To address this issue, in the proposed scheme image histogram is quantized into a different number of bins which represents the low dimensional feature descriptor effectively. Since, the global and local features play an important role in image retrieval, therefore, considering any single feature for image retrieval is not adequate, so in this paper, a quantized histogram-based global and local features have been considered for feature representation. To avoid variations among the feature components, suitable weights are assigned to the local and global features effectively. To check the efficacy of the proposed method, performance analysis using a different number of bins has been evaluated based on two standard similarity distances for corel-1 K image dataset. The presented work has achieved satisfactory retrieval results in terms of precision, recall, and F-score metrics.