Enabling Region-Specific Control via Lassos in Point-Based Colorization

2024-12-18

arXiv.org

AI Summary

To address “color collapse”—a problem in intelligent local colorization where ambiguous boundaries cause color bleeding across semantically similar regions—this paper proposes a lasso-guided attention localization framework. The method introduces geometric lasso interactions into point-based coloring for the first time, replacing dense point prompts with explicit region-level constraints. It comprises a lasso-driven attention mask generation module and a joint point-region propagation network. By modeling user-provided lassos as spatial priors, the framework achieves precise spatial localization and effectively suppresses color leakage. Experimental results demonstrate that, while preserving edge coherence and semantic fidelity, a single lasso interaction is equivalent to 4.18 point prompts in efficacy, yielding a 30% improvement in coloring efficiency.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Local Search
• Natural Language Processing: Prompt Engineering / Prompting

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Point-based interactive colorization techniques allow users to effortlessly colorize grayscale images using user-provided color hints. However, point-based methods often face challenges when different colors are given to semantically similar areas, leading to color intermingling and unsatisfactory results-an issue we refer to as color collapse. The fundamental cause of color collapse is the inadequacy of points for defining the boundaries for each color. To mitigate color collapse, we introduce a lasso tool that can control the scope of each color hint. Additionally, we design a framework that leverages the user-provided lassos to localize the attention masks. The experimental results show that using a single lasso is as effective as applying 4.18 individual color hints and can achieve the desired outcomes in 30% less time than using points alone.

Problem

• Color Collapse
• Image Segmentation
• Smart Coloring Tools

Innovation

• Lasso Tool
• Coloring Accuracy
• Efficiency Improvement