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Recognizing visual objects in images, and actions in videos, are important problems in computer vision, with many applications in security, commerce, human-compute interaction, content-based video retrieval, visual surveillance, analysis of sports events and more. Recognition is mainly divided into two parts: category recognition (classification) and detection/localization. The goal of category recognition is to classify a given object (or action) into one of several pre-specified categories, while object (action) detection is meant to separate objects (actions) of interest from the background in a target image or video. Typically, learning-based approaches involve generative or discriminative training models (parametric models) for each category based on training examples. These methods require a large number of training examples, can result in over-fitting of parameters, and do not scale well with the number of object (or action) categories. We have developed a framework where problems such as generic object detection, action detection, and action category classification can be solved in a unified setting from a single example (i.e. without training.) In a related effort, we have also developed a method which can accurately detect salient objects or actions from visual data without any background or prior knowledge. Here is a recent talk that summarizes these ideas. For additional results and graphic explanations, please visit the project webpages for object detection; action recognition ; and saliency detection. Also, please consult the relevant publications below.

Related Journal Publications

1. H.J. Seo and P. Milanfar, “ Action Recognition from One Example”, Submitted to IEEE Trans. on Pattern Analysis and Machine Intelligence
2. H.J. Seo and P. Milanfar, “ Static and Space-time Visual Saliency Detection by Self-Resemblance”, Accepted for publication in  The Journal of Vision
3. H.J. Seo and P. Milanfar, “ Training-free, Generic Object Detection using Locally Adaptive Regression Kernels”, Accepted for publication in IEEE Trans. on Pattern Analysis and Machine Intelligence

Related Conference Publications and Presentations

1. H.J. Seo, and P. Milanfar, “ Detection of Human Actions From A Single Example ”, IEEE International Conference on Computer Vision (ICCV), Kyoto, September, 2009
2. H.J. Seo, and P. Milanfar, “ Nonparametric Bottom-Up Saliency Detection by Self-Resemblance ”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1st International Workshop on Visual Scene Understanding (ViSU’09), Miami, June, 2009
3. H. Seo, and P. Milanfar, “ Using Local Regression Kernels for Statistical Object Detection ”, Proceedings of IEEE International Conference on Image Processing (ICIP), San Diego, CA, October 2008.