Code/Software/Datasets

The page lists some source code/software/datasets from some of our research projects and papers. You are free to download them for your own research, provided that you cite the source paper explicitly in your work, do not make any redistribution and do not use them for commercial purposes.

Source Code

• VSEC-LDA: Boosting Topic Modeling with Embedded Vocabulary Selection: click to download
  • This package is based on our following paper:
  • Y. Ding and B. Li, “VSEC-LDA: Boosting Topic Modeling with Embedded Vocabulary Selection”, full manuscript available here https://arxiv.org/abs/2001.05578
• Recognizing Video Events with Varying Rhythms: click to download
  • This package is based on our following paper:
  • Y. Li, T. Yu, and B. Li, “Recognizing Video Events with Varying Rhythms”, full manuscript available here https://arxiv.org/abs/2001.05060
• Weakly Supervised Deep Image Hashing using Tag Embedding: click to download
  • (Note: In the current implementation, we used ResNet50 of the Keras library instead of AlexNet as reported in the paper. This is due to unavailability of AlexNet model in Keras. Consequently, we are able to achieve slightly better accuracy than reported in the paper.)
  • This package is based on our following publication:
  • V. Gattupalli, Y. Zhuo, B. Li, “Weakly Supervised Deep Image Hashing using Tag Embedding”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 10375-10384.
• Data-adaptive low-rank modeling and external gradient prior for single image super-resolution: click to download
  • This package is based on our following publication:
  • Kan Chang, Xueyu Zhang, Pak Lun Kevin Ding, Baoxin Li, “Data-adaptive low-rank modeling and external gradient prior for single image super-resolution”, Signal Processing 161: 36-49 (2019).
• Single Image Super-resolution Using Joint Regularization: click to download
  • This package is based on our following publication:
  • Chang K, Ding P L K, Li B. “Single Image Super Resolution Using Joint Regularization”, IEEE Signal Processing Letters, 2018, 25(4): 596-600.
• Single image super-resolution using collaborative representation and non-local self-similarity: click to download
  • This package is based on our following publication:
  • Chang K, Ding P L K, Li B. “Single image super-resolution using collaborative representation and non-local self-similarity”, Signal Processing, 2018, 149:49-61.
• Compressive Sensing Reconstruction of Correlated Images Using Joint Regularization: click to download
  • This package is based on our following publication:
  • Kan Chang, Pak Lun Kevin Ding, and Baoxin Li, “Compressive Sensing Reconstruction of Correlated Images Using Joint Regularization”, IEEE Signal Processing Letters (SPL), vol.23, no.4, pp.449-453, Apr. 2016.
• Joint modeling and reconstruction of a compressively-sensed set of correlated images: click to download
  • This package is based on our following publication:
  • K. Chang and B. Li, “Joint modeling and reconstruction of a compressively-sensed set of correlated images”, Journal of Visual Communication and Image Representation, Vol. 33, pp. 286-300. November, 2015.
• Color image demosaicking using inter-channel correlation and nonlocal self-similarity: click to download
  • This package is based on our following publication:
  • K. Chang, P.L.K. Ding and B. Li, “Joint modeling and reconSignal Processing: Image Communication, Vol. 39, pp. 264-279. 2015.
• Relative multi-task learning for predicting multiple attributes: click to download
  • This package is based on our following publication:
  • L. Chen, Q. Zhang, and B. Li, “Predicting Multiple Attributes via Relative Multi-task Learning”, IEEE International Conference on Computer Vision & Pattern Recognition (CVPR), June, 2014.
• Relative Hidden Markov Model: click to download
  • This package is based on our following publications:(The code corresponds to the journal paper, which is an improved version of the CVPR paper).
  • Q. Zhang and B. Li, “Relative Hidden Markov Models for Video-based Evaluation of Motion Skills in Surgical Training”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2014.
  • and
  • Qiang Zhang & Baoxin Li, “Relative Hidden Markov Model for Skill Evaluation”, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June, 2013.
• Joint Sparsity Model with Matrix Completion for Face Recognition: click to download
  • This package is based on our following publication:
  • Q. Zhang and B. Li, “Mining Discriminative Components with Low-Rank and Sparsity Constraints for Face Recognition”, ACM Conf. on Knowledge Discover & Data Mining (SIG KDD) 2012.
• Discriminative Affine Sparse Codes for Image Classification: click to download
  • This package is based on our following publication:
  • Naveen Kulkarni and Baoxin Li, “Discriminative Affine Sparse Codes for Image Classification”, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June, 2011.
• Discriminative K-SVD: click to download
  • This package is based on our following publication:
  • Qiang Zhang and Baoxin Li, “Discriminative K-SVD for Dictionary Learning in Face Recognition”, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June, 2010.
• A Compressive Sensing Approach for Expression-Invariant Face Recognition: click to download
  • This package is based on our following publication:
  • Pradeep Nagesh and Baoxin Li, “A Compressive Sensing Approach for Expression-Invariant Face Recognition”, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June, 2009.

Software

• Eye Detector: Source code available upon request.

Datasets

• Images with Ground Truth for Evaluating Text Detection in Floor Maps: click to download
  • This dataset accompanies our following publication:
  • H. B. Maguluri, Q. Tian, B. Li, “Detecting Text in Floor Maps Using Histogram of Oriented Gradients”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2013.
• 300 Images with Contour-based Ground Truth for Evaluating Salient Object Detection: click to download
  • This dataset accompanies our following publication:
  • Zheshen Wang, Baoxin Li, “A Two-stage Approach to Saliency Detection”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2008.