Beyond IID: Learning to Combine Non-IID Metrics for Vision Tasks
Authors: Yinghuan Shi, Wenbin Li, Yang Gao, Longbing Cao, Dinggang Shen
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results show that learning and integrating non-IID metrics improves the performance, compared to the IID methods. Moreover, our method achieves results comparable or better than that of the state-of-the-arts. Table 1: The error rates of all comparison methods. Table 2: The results of Accuracy (AC), Specificity (SP), Sensitivity (SE), F1 Score, and AUC of all methods. |
| Researcher Affiliation | Academia | State Key Laboratory for Novel Software Technology, Nanjing University, China Advanced Analytics Institute, University of Technology at Sydney, Australia Department of Radiology and BRIC, UNC-Chapel Hill, USA |
| Pseudocode | Yes | Algorithm 1 NIME-CK Input: Kp, φij and yij. Output: Ω, wp (p = 1, ..., P). 1: wp 1 1 P 2: Ω1 Kernel PCA Initialization (2007) 3: while not converge do 4: Ωt+1 Ωt ρΓt in Eqn.(8) 5: wp t+1 Solved in Eqn.(10) by SA 6: end while |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Natural Image Segmentation We evaluate the NIME models against various segmentation methods on the MSRC image set (Rother, Kolmogorov, and Blake 2004), which is a challenging and commonly-used image set in image segmentation and with results available from many existing methods for comparison. |
| Dataset Splits | Yes | All the parameters (e.g., λ) are experimentally determined by inner cross validation. 10-fold cross validation was taken for all the baseline and our methods. All the parameters (e.g., λ) were experimentally determined by inner cross validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, or cloud instances) used to run the experiments. |
| Software Dependencies | No | The paper mentions software components like SLIC and Le Net but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For experimental settings, for each superpixel, we choose its adjacent superpixels as the spatial neighbors. For the step of superpixel over-segmentation, we employ SLIC (Achanta et al. 2012) to over-segment each image into a number of non-overlapping superpixels (typically 500-1500 superpixels). For feature representation, we extract LBP (30dimensional), Gabor (48-dimensional), color (including histogram, mean, variance, with totally 66-dimensional), and intensity (4-dimensional). In total, to represent a superpixel, we extract 148-dimensional features. All the parameters (e.g., λ) are experimentally determined by inner cross validation. In non-IID metric learning, for each current cell, we empirically choose its top 5 closest cells as the spatial neighbors. |