Face Sketch Synthesis Style Similarity:A New Structure Co-occurrence Texture Measure
Add this Pith Number to your LaTeX paper
What is a Pith Number?\usepackage{pith}
\pithnumber{HGVGNB7Z}
Prints a linked pith:HGVGNB7Z badge after your title and writes the identifier into PDF metadata. Compiles on arXiv with no extra files. Learn more
read the original abstract
Existing face sketch synthesis (FSS) similarity measures are sensitive to slight image degradation (e.g., noise, blur). However, human perception of the similarity of two sketches will consider both structure and texture as essential factors and is not sensitive to slight ("pixel-level") mismatches. Consequently, the use of existing similarity measures can lead to better algorithms receiving a lower score than worse algorithms. This unreliable evaluation has significantly hindered the development of the FSS field. To solve this problem, we propose a novel and robust style similarity measure called Scoot-measure (Structure CO-Occurrence Texture Measure), which simultaneously evaluates "block-level" spatial structure and co-occurrence texture statistics. In addition, we further propose 4 new meta-measures and create 2 new datasets to perform a comprehensive evaluation of several widely-used FSS measures on two large databases. Experimental results demonstrate that our measure not only provides a reliable evaluation but also achieves significantly improved performance. Specifically, the study indicated a higher degree (78.8%) of correlation between our measure and human judgment than the best prior measure (58.6%). Our code will be made available.
This paper has not been read by Pith yet.
Forward citations
Cited by 4 Pith papers
-
VesselRW: Weakly Supervised Subcutaneous Vessel Segmentation via Learned Random Walk Propagation
VesselRW expands sparse vessel annotations into dense probabilistic supervision via a jointly trained differentiable random walk model with uncertainty weighting and topology regularization for CNN-based subcutaneous ...
-
DualResolution Residual Architecture with Artifact Suppression for Melanocytic Lesion Segmentation
Dual-resolution residual architecture with boundary-aware connections, channel attention, artifact suppression, and combined Dice-Tversky plus boundary and contrastive losses improves lesion boundary precision over st...
-
Edge Detection for Organ Boundaries via Top Down Refinement and SubPixel Upsampling
A top-down backward refinement network with subpixel upsampling generates crisp high-resolution organ boundaries in medical images and improves downstream segmentation and registration performance.
-
Deeply Dual Supervised learning for melanoma recognition
A dual-pathway deep learning model with attention mechanisms and multi-scale feature aggregation claims superior accuracy and fewer false positives for melanoma detection on benchmark datasets.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.