Object Detection with Discriminatively Trained Part-Based Models
We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL datasets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI-SVM in terms of latent variables. A latent SVM is semi-convex and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.
Text ReferencePedro Felzenszwalb, Ross Girshick, David McAllester, and Deva Ramanan. Object detection with discriminatively trained part-based models. In IEEE Pattern Analysis and Machine Intelligence (PAMI). 2009.
author = "Felzenszwalb, Pedro and Girshick, Ross and McAllester, David and Ramanan, Deva",
booktitle = "IEEE Pattern Analysis and Machine Intelligence (PAMI)",
title = "Object Detection with Discriminatively Trained Part-Based Models",
year = "2009",
tag = "object_recognition"