PSSI-MaxST: An Efficient Pixel-Segment Similarity Index Using Intensity and Smoothness Features for Maximum Spanning Tree Based Segmentation

2026-01-15

AI Summary

This work proposes an efficient interactive image segmentation framework based on a Pixel-Superpixel Similarity Index (PSSI) to address the limitations of existing methods, which often suffer from high computational cost, sensitivity to user input, and performance degradation when foreground and background exhibit similar colors. The approach leverages MeanShift to generate initial superpixels, constructs a pixel-superpixel graph, and employs a Maximum Spanning Tree (MaxST) for segmentation, jointly modeling color, texture, shape, and local strong connectivity. A key innovation lies in computing PSSI via the harmonic mean of multi-channel similarities, effectively mitigating inconsistencies from individual channels and enhancing robustness. Notably, PSSI achieves a computational complexity of only O(B), significantly improving efficiency. Experimental results on the GrabCut and Images250 datasets demonstrate superior performance over state-of-the-art methods—including AMOE, OneCut, and SSNCut—in terms of Jaccard index, F1 score, execution time, and average error.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Semi-Supervised Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Interactive graph-based segmentation methods partition an image into foreground and background regions with the aid of user inputs. However, existing approaches often suffer from high computational costs, sensitivity to user interactions, and degraded performance when the foreground and background share similar color distributions. A key factor influencing segmentation performance is the similarity measure used for assigning edge weights in the graph. To address these challenges, we propose a novel Pixel Segment Similarity Index (PSSI), which leverages the harmonic mean of inter-channel similarities by incorporating both pixel intensity and spatial smoothness features. The harmonic mean effectively penalizes dissimilarities in any individual channel, enhancing robustness. The computational complexity of PSSI is $\mathcal{O}(B)$, where $B$ denotes the number of histogram bins. Our segmentation framework begins with low-level segmentation using MeanShift, which effectively captures color, texture, and segment shape. Based on the resulting pixel segments, we construct a pixel-segment graph with edge weights determined by PSSI. For partitioning, we employ the Maximum Spanning Tree (MaxST), which captures strongly connected local neighborhoods beneficial for precise segmentation. The integration of the proposed PSSI, MeanShift, and MaxST allows our method to jointly capture color similarity, smoothness, texture, shape, and strong local connectivity. Experimental evaluations on the GrabCut and Images250 datasets demonstrate that our method consistently outperforms current graph-based interactive segmentation methods such as AMOE, OneCut, and SSNCut in terms of segmentation quality, as measured by Jaccard Index (IoU), $F_1$ score, execution time and Mean Error (ME). Code is publicly available at: https://github.com/KaustubhShejole/PSSI-MaxST.

Problem

• interactive segmentation
• graph-based segmentation
• similarity measure
• color similarity
• computational cost

Innovation

• Pixel-Segment Similarity Index
• Maximum Spanning Tree
• Interactive Segmentation
• Harmonic Mean Similarity
• MeanShift