Distribution Methodology for Objects Extraction from Complex Network and Colorization

Distribution colorization in complex networks refers to a method or technique that utilizes advanced algorithms and deep learning models within a complex network architecture to convert a grayscale video into a color video. This approach focuses on analyzing the distribution of colors in a dataset of color images and leveraging this information to infer and assign appropriate colors to the corresponding grayscale video frames. The method typically involves the following steps: (a) Dataset Preparation: A large dataset of color images is collected, containing diverse scenes and objects with their corresponding color information. This dataset serves as the training data for the complex network. (b) Network Architecture: A complex network is designed and trained using deep learning techniques, such as convolutional neural networks (CNNs) or generative adversarial networks (GANs). The network learns to understand the relationships between grayscale video frames and their corresponding color images by analyzing the distribution of colors in the training dataset. (c) Feature Extraction: The grayscale video frames are processed through the network to extract relevant features. These features capture the visual characteristics and patterns of the grayscale frames. (d) Color Inference: The network utilizes the extracted features and the learned distribution of colors from the training dataset to infer the most probable colors for each grayscale frame. This process involves mapping the grayscale values to appropriate color values based on the learned color distribution. (e) Colorization: The inferred color values are applied to the grayscale frames, resulting in the conversion of the grayscale video into a color video. The network ensures that the assigned colors are coherent and visually plausible, maintaining the overall aesthetics of the original content. And (f) Post-processing: Additional post-processing techniques may be applied to refine the colorized video, such as color correction, noise reduction, and temporal coherence enhancement. The complex network in distribution colorization plays a crucial role in leveraging the learned color distribution and patterns from the training dataset to accurately assign colors to the grayscale video frames. It utilizes the power of deep learning and advanced algorithms to capture complex relationships between grayscale and color video data, enabling the generation of visually appealing and realistic colorized videos. Typically, a digital video clip is one of the recording media that contain huge amounts of information ranging from stories, audio/video music to medical and biological scans. Also, colour video clip presents more interested details compared to Gray scale as one would find in spatially in medical diagnosis scans. Each video clip has sequence of frames, and each frame have sequence of objects. But the main challenge in this field is how to find the best method to transform the Gray scale digital video clip into colour to make it more attractive and meaningful? In this work, we suggest deploying data mining methodology as tool to meet this challenge. The data miner methodology and its underlying technique successfully achieves the colorization task on video clips of different in size, number of frames and number of objects in the frames 87% of the time. Its application is most prominent in the medical imaging field, and easily extended to other fields like weather forecasting.