Planar Ultrametrics for Image Segmentation
Authors: Julian E. Yarkony, Charless Fowlkes
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our algorithm to the task of natural image segmentation and demonstrate that our algorithm converges rapidly and produces optimal or near-optimal solutions in practice. We applied our algorithm to segmenting images from the Berkeley Segmentation Data set (BSDS) [16]. |
| Researcher Affiliation | Collaboration | Julian Yarkony Experian Data Lab San Diego, CA 92130 julian.yarkony@experian.com Charless C. Fowlkes Department of Computer Science University of California Irvine fowlkes@ics.uci.edu |
| Pseudocode | Yes | Algorithm 1 Dual Closest Ultrametric via Cutting Planes |
| Open Source Code | No | The paper mentions using existing libraries (Blossom V, IBM ILOG CPLEX) but does not state that their own code for the described methodology is open-source or provide a link. |
| Open Datasets | Yes | We applied our algorithm to segmenting images from the Berkeley Segmentation Data set (BSDS) [16]. |
| Dataset Splits | No | The paper references the BSDS dataset but does not explicitly provide details about training, validation, or test dataset splits (percentages, counts, or specific predefined split usage). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions "MATLAB", "Blossom V implementation of minimum-weight perfect matching [15]" and "IBM ILOG CPLEX LP solver" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We truncate extreme values to enforce that g Pbe [ϵ, 1 ϵ] with ϵ = 0.001 and set θe = log g P be 1 g P be. In our experiments we use a fixed set of eleven distance threshold levels {δl} chosen to uniformly span the useful range of threshold values [9.6, 12.6]. We performed dual cutting plane iterations until convergence or 2000 seconds had passed. ... We terminate when the total residual is greater than 2 10 4. In our experiments, this penalty is scaled by a parameter ϵ = 10 4. In our implementation we test a few discrete thresholds t {0, 0.2, 0.4, 0.6, 0.8} and take that threshold that yields X with the lowest cost. |