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Author
Marçal Rusiñol; Volkmar Frinken; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados
Title
Multimodal page classification in administrative document image streams
Type
Journal Article
Year
2014
Publication
International Journal on Document Analysis and Recognition
Abbreviated Journal
IJDAR
Volume
17
Issue
4
Pages
331-341
Keywords
Digital mail room; Multimodal page classification; Visual and textual document description
Abstract
In this paper, we present a page classification application in a banking workflow. The proposed architecture represents administrative document images by merging visual and textual descriptions. The visual description is based on a hierarchical representation of the pixel intensity distribution. The textual description uses latent semantic analysis to represent document content as a mixture of topics. Several off-the-shelf classifiers and different strategies for combining visual and textual cues have been evaluated. A final step uses an n-gram model of the page stream allowing a finer-grained classification of pages. The proposed method has been tested in a real large-scale environment and we report results on a dataset of 70,000 pages.
Address
Corporate Author
Thesis
Publisher
Springer Berlin Heidelberg
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
1433-2833
ISBN
Medium
Area
Expedition
Conference
Notes
DAG; LAMP; 600.056; 600.061; 601.240; 601.223; 600.077; 600.079
Approved
no
Call Number
Admin @ si @ RFK2014
Serial
2523
Permanent link to this record
Author
Lorenzo Seidenari; Giuseppe Serra; Andrew Bagdanov; Alberto del Bimbo
Title
Local pyramidal descriptors for image recognition
Type
Journal Article
Year
2014
Publication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Abbreviated Journal
TPAMI
Volume
36
Issue
5
Pages
1033 - 1040
Keywords
Object categorization; local features; kernel methods
Abstract
In this paper we present a novel method to improve the flexibility of descriptor matching for image recognition by using local multiresolution
pyramids in feature space. We propose that image patches be represented at multiple levels of descriptor detail and that these levels be defined in terms of local spatial pooling resolution. Preserving multiple levels of detail in local descriptors is a way of hedging one’s bets on which levels will most relevant for matching during learning and recognition. We introduce the Pyramid SIFT (P-SIFT) descriptor and show that its use in four state-of-the-art image recognition pipelines improves accuracy and yields state-of-the-art results. Our technique is applicable independently of spatial pyramid matching and we show that spatial pyramids can be combined with local pyramids to obtain
further improvement.We achieve state-of-the-art results on Caltech-101
(80.1%) and Caltech-256 (52.6%) when compared to other approaches based on SIFT features over intensity images. Our technique is efficient and is extremely easy to integrate into image recognition pipelines.
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
0162-8828
ISBN
Medium
Area
Expedition
Conference
Notes
LAMP; 600.079
Approved
no
Call Number
Admin @ si @ SSB2014
Serial
2524
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