A fully automated colorization model that integrates image segmentation features to enhance both the accuracy and diversity of colorization is proposed. In the model, a multipath architecture is employed, with each path designed to address a specific objective in processing grayscale input images. The context path utilizes a pretrained ResNet50 model to identify object classes while the spatial path determines the locations of these objects. ResNet50 is a 50-layer deep convolutional neural network (CNN) that uses skip connections to address the challenges of training deep models. It is widely applied in image classification and feature extraction. The outputs from both paths are subsequently fused and fed into the colorization network to ensure precise representation of image structures and to prevent color spillover across object boundaries. The colorization network is designed to handle high-resolution inputs, enabling accurate colorization of small objects and enhancing overall color diversity. The proposed model demonstrates robust performance even when training with small datasets. Comparative evaluations with CNN-based and diffusion-based classification approaches show that the proposed model significantly improves colorization quality.