IEEE Trans Pattern Anal Mach Intell - Improved Object Categorization and Detection Using Comparative Object Similarity.

Tópicos

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Resumo

Due to the intrinsic long-tailed distribution of objects in the real world, we are unlikely to be able to train an object recognizer/ detector with many visual examples for each category. We have to share visual knowledge between object categories to enable learning with few or no training examples. In this paper, we show that local object similarity information — statements that pairs of categories are similar or dissimilar — is a very useful cue to tie different categories to each other for effective knowledge transfer. The key insight: given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. To exploit this category dependent similarity regularization, we develop a regularized kernel machine algorithm to train kernel classifiers for categories with few or no training examples. We also adapt the state-ofthe- art object detector [10] to encode object similarity constraints. Our experiments on hundreds of categories from the Labelme dataset show that our regularized kernel classifiers can make significant improvement on object categorization. We also evaluate the improved object detector on the PASCAL VOC 2007 benchmark dataset.

Resumo Limpo

due intrins longtail distribut object real world unlik abl train object recogn detector mani visual exampl categori share visual knowledg object categori enabl learn train exampl paper show local object similar inform x statement pair categori similar dissimilar x use cue tie differ categori effect knowledg transfer key insight given set object categori similar set categori dissimilar good object model respond strong exampl similar categori exampl dissimilar categori exploit categori depend similar regular develop regular kernel machin algorithm train kernel classifi categori train exampl also adapt stateofth art object detector encod object similar constraint experi hundr categori labelm dataset show regular kernel classifi can make signific improv object categor also evalu improv object detector pascal voc benchmark dataset

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