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Visual Representations for Fine-grai...

Zhang, Ning.

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Visual Representations for Fine-grained Categorization.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Visual Representations for Fine-grained Categorization./
Author:	Zhang, Ning.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2015,
Description:	83 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-08(E), Section: B.
Contained By:	Dissertation Abstracts International77-08B(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10086169
ISBN:	9781339593029

Visual Representations for Fine-grained Categorization.
Zhang, Ning.

Visual Representations for Fine-grained Categorization. - Ann Arbor : ProQuest Dissertations & Theses, 2015 - 83 p.

Source: Dissertation Abstracts International, Volume: 77-08(E), Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2015.

In contrast to basic-level object recognition, fine-grained categorization aims to distinguish between subordinate categories, such as different animal breeds or species, plant species or man-made product models. The problem can be extremely challenging due to the subtle differences in the appearance of certain parts across related categories and often requires distinctions that must be conditioned on the object pose for reliable identification. Discriminative markings are often highly localized, leading traditional object recognition approaches to struggle with the large pose variations often present in these domains. Face recognition is the classic case of fine-grained recognition, and it is noteworthy that the best face recognition methods jointly discover facial landmarks and extract features from those locations. We propose pose-normalized representations, which align training exemplars, either piecewise by part or globally for the whole object, effectively factoring out differences in pose and in camera viewing angle.

ISBN: 9781339593029Subjects--Topical Terms:

523869
Computer science.

Visual Representations for Fine-grained Categorization.
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245 1 0 $a Visual Representations for Fine-grained Categorization.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2015
300 $a 83 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-08(E), Section: B.
500 $a Adviser: Trevor Darrell.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2015.
520 $a In contrast to basic-level object recognition, fine-grained categorization aims to distinguish between subordinate categories, such as different animal breeds or species, plant species or man-made product models. The problem can be extremely challenging due to the subtle differences in the appearance of certain parts across related categories and often requires distinctions that must be conditioned on the object pose for reliable identification. Discriminative markings are often highly localized, leading traditional object recognition approaches to struggle with the large pose variations often present in these domains. Face recognition is the classic case of fine-grained recognition, and it is noteworthy that the best face recognition methods jointly discover facial landmarks and extract features from those locations. We propose pose-normalized representations, which align training exemplars, either piecewise by part or globally for the whole object, effectively factoring out differences in pose and in camera viewing angle.
520 $a I first present the methods of using the idea of pose-normalization for two related applications: human attribute classification and person recognition beyond frontal face. Following the recent success of deep learning, we use deep convolutional features as feature representations. Next, I will introduce the part-based RCNN method as an extension of state-of-art object detection method RCNN for fine-grained categorization. The model learns both whole-object and part detectors, and enforces learned geometric constraints between them. I will also show the results of using the recent compact bilinear features to generate the pose-normalized representations. However, bottom-up region proposals is limited by hand-engineered features and in the final work, I will present a fully convolution deep network, trained end-to-end for part localization and fine-grained classification.
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