Patch-Based Deep Unsupervised Image Segmentation using Graph Cuts
- URL: http://arxiv.org/abs/2311.01475v2
- Date: Mon, 15 Jan 2024 21:03:53 GMT
- Title: Patch-Based Deep Unsupervised Image Segmentation using Graph Cuts
- Authors: Isaac Wasserman and Jeova Farias Sales Rocha Neto
- Abstract summary: We propose a patch-based unsupervised image segmentation strategy that bridges advances in unsupervised feature extraction with the algorithmic help of classical graph-based methods.
We show that a simple convolutional neural network, trained to classify image patches, naturally leads to a state-of-the-art fully-convolutional unsupervised pixel-level segmenter.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Unsupervised image segmentation aims at grouping different semantic patterns
in an image without the use of human annotation. Similarly, image clustering
searches for groupings of images based on their semantic content without
supervision. Classically, both problems have captivated researchers as they
drew from sound mathematical concepts to produce concrete applications. With
the emergence of deep learning, the scientific community turned its attention
to complex neural network-based solvers that achieved impressive results in
those domains but rarely leveraged the advances made by classical methods. In
this work, we propose a patch-based unsupervised image segmentation strategy
that bridges advances in unsupervised feature extraction from deep clustering
methods with the algorithmic help of classical graph-based methods. We show
that a simple convolutional neural network, trained to classify image patches
and iteratively regularized using graph cuts, naturally leads to a
state-of-the-art fully-convolutional unsupervised pixel-level segmenter.
Furthermore, we demonstrate that this is the ideal setting for leveraging the
patch-level pairwise features generated by vision transformer models. Our
results on real image data demonstrate the effectiveness of our proposed
methodology.
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