Latent Semantic Representation Learning for Scene Classification
Authors: Xin Li, Yuhong Guo
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on standard scene recognition tasks demonstrate the efficacy of the proposed approach, comparing to the state-of-the-art scene recognition methods. |
| Researcher Affiliation | Academia | Xin Li XINLI@TEMPLE.EDU Yuhong Guo YUHONG@TEMPLE.EDU Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA |
| Pseudocode | Yes | Algorithm 1 Projected gradient descent algorithm |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We evaluated the proposed method on 3 standard scene datasets: MIT Label Me Urban and Natural Scene (Label Me) (Oliva & Torralba, 2001), 15 Natural Scene dataset (Lazebnik et al., 2006) (Scene 15) and UIUC Sports (Li & Fei-Fei, 2007). |
| Dataset Splits | Yes | In all experiments, we randomly selected 80 images per category for training and used the rest for testing for all methods except the convolutional neural networks which need more training data. [...] In each experiment, we used 5-fold cross-validation technique to select the trade-off parameters for all methods. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions types of models and general approaches but does not provide specific software library names with version numbers, such as "Python 3.x" or "TensorFlow X.Y.Z". |
| Experiment Setup | Yes | For the proposed method, we conducted parameter selection for the trade-off parameters γg and γz from the set [0.005, 0.05, 0.1, 0.5, 1, 5], and performed selection for µ from the set [0.1, 0.5, 1, 5, 10], while setting γf = 0.5 and all {αi} as 1. We treated each image as a bag of 16 16 patches and extracted a HOG feature vector with length 72 (Dalal & Triggs, 2005) from each patch. We further normalized each HOG vector to have unit L2-norm. |