Image classification using object detectors

Thibaut Durand (1) , Nicolas Thome (1) , Matthieu Cord (1) , Sandra Avila (2, 1)

1 MLIA - Machine Learning and Information Access
2 UFMG - Universidade Federal de Minas Gerais = Federal University of Minas Gerais [Belo Horizonte, Brazil]

Image categorization is one of the most competitive topic in computer vision and image processing. In this paper, we propose to use trained object and region detectors to represent the visual content of each image. Compared to similar methods found in the literature, our method encompasses two main areas of novelty: introducing a new spatial pooling formalism and designing a late fusion strategy for combining our rep-resentation with state-of-the art methods based on low-level descriptors, e.g. Fisher Vectors and BossaNova. Our experiments carried out in the challenging PASCAL VOC 2007 dataset reveal outstanding performances. When combined with low-level representations, we reach more than 67.6% in MAP, outperforming recently reported results in this dataset with a large margin.

Mots clés: Image Categorization; Object/Region Detectors; Spatial pooling; Combination with low-level Representations

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Thibaut Durand, Nicolas Thome, Matthieu Cord, Sandra Avila. Image classification using object detectors. IEEE International Conference on Image Processing, Sep 2013, Melbourne, Australia. pp.4340 - 4344, ⟨10.1109/ICIP.2013.6738894⟩. ⟨hal-01078079⟩