Face Clustering in Videos with Proportion Prior
Authors: Zhiqiang Tang, Yifan Zhang, Zechao Li, Hanqing Lu
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments on a public data set from realworld videos, we observe improvements on clustering performance against state-of-the-art methods. |
| Researcher Affiliation | Academia | Zhiqiang Tang1, Yifan Zhang1 , Zechao Li2, Hanqing Lu1 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2School of Computer Science and Engineering, Nanjing University of Science and Technology zqtang2013@gmail.com, {yfzhang, luhq}@nlpr.ia.ac.cn, zechao.li@njust.edu.cn |
| Pseudocode | Yes | Algorithm 1 EM for Hidden Conditional Random Field |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code for their methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluate the performance of our method in the public face data set Big Bang Theory(BBT) given in [Bauml et al., 2013]. |
| Dataset Splits | No | The paper describes sampling faces and computing track labels, but does not explicitly detail a training, validation, and test dataset split for model development or evaluation in the traditional sense, as it's a clustering task. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | We set λ1 = 10 4 in the experiments. λ2 is set to 10 4 in the experiments. ... the k-nearest neighbor graph is used and k = 10. ... The feature of each face is represented by a 240 dimensional Discrete Cosine Transform (DCT) vector. ... Then the Laplacian Eigenmaps reduces the feature dimension from 240 to the cluster number. |