عنوان انگلیسی مقاله:
Simultaneous learning of reduced prototypes and local metric for image set classification
ترجمه فارسی عنوان مقاله:
یادگیری همزمان از نمونه های اولیه کاهش یافته و متریک محلی برای طبقه بندی مجموعه تصویر
Sciencedirect - Elsevier - Expert Systems With Applications, 134 (2019) 102-111: doi:10:1016/j:eswa:2019:05:025
Zhenwen Ren a , b , ∗, Bin Wu c , Quansen Sun b , Mingna Wu a
Classification based on image set is recently a competitive technique, where each set contains multiple images of a person or an object. As a widely used model, affine hull has shown its power in modeling image set. However, due to the existence of noise and outliers, the over-large affine hull usually matches fails when two hulls overlapped. Aiming at alleviating this handicap, this paper proposes a novel method for image set classification, namely Learning of Reduced Prototypes and Local Metric (LRPLM). Specifi- cally, for each gallery image set, a reduced set of prototypes and an optimal local feature-wise metric are simultaneously learned, which jointly minimize the loss function involved the estimation of classifi- cation error probability. In doing so, LRPLM inherits the merits of affine hull with better representation to account for the unseen appearances and makes use of the powerful discriminative ability improved by the local metric. It looks like that LRPLM pulls similar image sets with the same class label “closer”to each other, while pushing dissimilar ones “far away”. Extensive experiments illustrate the considerable effectiveness of LRPLM on three widely used datasets. As we know, classification is a research hotspot in expert and intelligent systems. Different from the previous classification methods, LRPLM focuses on im- age set-based classification technology, while most of them are single-shot classification technology. Thus, the proposed method can be considered as an expert system technology for medical diagnosis, security monitoring, object categorization, and biometrics recognition applications.
Keywords: Image set classification | Prototype learning | Metric learning | Face recognition | Expert system