000199813 001__ 199813
000199813 005__ 20190812205801.0
000199813 037__ $$aCONF
000199813 245__ $$aScene Recognition with Naive Bayes Non-linear Learning
000199813 269__ $$a2014
000199813 260__ $$c2014
000199813 336__ $$aConference Papers
000199813 520__ $$aA crucial feature of a good scene recognition algorithm is its ability to generalize. Scene categories, especially those related to human made indoor places or to human activities like sports, do present a high degree of intra-class variability, which in turn requires high robustness and generalization properties. Such features are amongst the distinctive characteristics of the Naive Bayes Nearest Neighbor (NBNN) approach, an image classification framework that since its introduction in 2008 has been gaining momentum in the visual recognition community. In this paper we show how with a straightforward modification of the original NBNN scoring function it is possible to use a recently introduced latent locally linear SVM algorithm to discriminatively learn a set of prototype local features for each class. The resulting classification algorithm, that we call Naive Bayes Non-linear Learning (NBNL) preserves the generality and robustness properties of the original approach, while greatly reducing its memory requirements during testing, and significantly improving its performance. To the best of our knowledge this is the first work to exploit the structure of the local features through the use of a latent locally linear discriminative learning method. Experiments over three different public scene recognition datasets show the effectiveness of the proposed algorithm, which outperforms several existing NBNN-based methods and is competitive with standard Bag-of-Words plus SVM approaches.
000199813 6531_ $$aDiscriminative Methods
000199813 6531_ $$aIndoor Scene Recognition
000199813 6531_ $$aLatent SVM
000199813 6531_ $$aLocally Linear Support Vector Machines
000199813 6531_ $$aML3
000199813 6531_ $$amulticlass classification
000199813 6531_ $$aNaive Bayes Nearest Neighbor
000199813 6531_ $$aScene Recognition
000199813 700__ $$0246038$$g197211$$aFornoni, Marco
000199813 700__ $$0243991$$g190271$$aCaputo, Barbara
000199813 7112_ $$aIEEE - Proceedings of the 22nd International Conference on Pattern Recognition
000199813 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/199813/files/Fornoni_ICPR_2014.pdf$$s499825
000199813 909C0 $$xU10381$$pLIDIAP$$0252189
000199813 909CO $$ooai:infoscience.tind.io:199813$$qGLOBAL_SET$$pconf$$pSTI
000199813 937__ $$aEPFL-CONF-199813
000199813 970__ $$aFornoni_ICPR_2014/LIDIAP
000199813 973__ $$aEPFL
000199813 980__ $$aCONF