Content-based image retrieval (CBIR) is an active area of research due to its wide applications. Most of the existing CBIR schemes are concentrated to do the searching of the images based on the texture, colour, or shape features extracted from the query image. In this manuscript, we propose an object detection-based CBIR scheme with quantised colour histograms. In the proposed scheme, the meaningful objects will be identified from the query image by using you only look once (YOLO) object detection techniques and the quantised histograms of each of the object categories. The object lists, their count, and the area covered by the objects along with quantised colour histograms will be used during feature matching to retrieve the related images from the large image pool. The experiment of the proposed scheme is carried on the Corel 1K and Caltech image dataset. We have observed an average precision of 0.96 during the experimental study which is quite high while comparing the precision from the well-known existing schemes.
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