Image Cosegmentation via Saliency-Guided Constrained Clustering with Cosine Similarity
Authors: Zhiqiang Tao, Hongfu Liu, Huazhu Fu, Yun Fu
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two widely-used datasets demonstrate our approach achieves competitive performance over the state-of-the-art cosegmentation methods. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Northeastern University, Boston, USA, 02115. 2Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore. 3College of Computer and Information Science, Northeastern University, Boston, USA, 02115. |
| Pseudocode | Yes | Algorithm 1 The algorithm of SGC3 for cosegmentation |
| Open Source Code | No | The paper does not provide a link or explicit statement about the availability of its source code. |
| Open Datasets | Yes | We conduct our experiment on two benchmark datasets, which are i Coseg dataset2 (Batra et al. 2011), and Internet dataset3 (Rubinstein et al. 2013), respectively. 2http://chenlab.ece.cornell.edu/projects/touch-coseg/ 3http://people.csail.mit.edu/mrub/Object Discovery/ |
| Dataset Splits | No | The paper describes using specific subsets of public datasets for evaluation (e.g., 'selects 31 image groups with 530 images' and '100 images per class'). However, it does not specify explicit training, validation, or test dataset splits in the typical supervised learning context (e.g., 80/10/10 percentages or separate subsets for these distinct phases). |
| Hardware Specification | Yes | All the experiments were conducted by MATLAB on a 64-bit Windows platform with two Intel Core i7 3.4GHz CPUs and 32GB RAM. |
| Software Dependencies | No | The paper mentions 'MATLAB' but does not provide a specific version number or version numbers for any other software libraries or dependencies used. |
| Experiment Setup | Yes | The weights of these three descriptors are set to be 0.6, 0.2 and 0.2, respectively. Moreover, we set the λ in Eq. (2) as 1e3 as the default setting. ...for each individual descriptor, we obtained 300 words by perform K-means clustering on each image group with the superpixel-level feature. |