Title :
Learning Discriminative Part Detectors for Image Classification and Cosegmentation
Author :
Jian Sun ; Ponce, J.
Author_Institution :
INRIA, Xi´an Jiaotong Univ., Xi´an, China
Abstract :
In this paper, we address the problem of learning discriminative part detectors from image sets with category labels. We propose a novel latent SVM model regularized by group sparsity to learn these part detectors. Starting from a large set of initial parts, the group sparsity regularizer forces the model to jointly select and optimize a set of discriminative part detectors in a max-margin framework. We propose a stochastic version of a proximal algorithm to solve the corresponding optimization problem. We apply the proposed method to image classification and co segmentation, and quantitative experiments with standard benchmarks show that it matches or improves upon the state of the art.
Keywords :
image classification; image segmentation; learning (artificial intelligence); object detection; optimisation; support vector machines; category labels; discriminative part detector learning; group sparsity regularizer; image classification; image cosegmentation; latent SVM model; max-margin framework; optimization problem; proximal algorithm; Cost function; Detectors; Image color analysis; Image segmentation; Support vector machines; Training;
Conference_Titel :
Computer Vision (ICCV), 2013 IEEE International Conference on
Conference_Location :
Sydney, NSW
DOI :
10.1109/ICCV.2013.422