3,803 research outputs found
View subspaces for indexing and retrieval of 3D models
View-based indexing schemes for 3D object retrieval are gaining popularity
since they provide good retrieval results. These schemes are coherent with the
theory that humans recognize objects based on their 2D appearances. The
viewbased techniques also allow users to search with various queries such as
binary images, range images and even 2D sketches. The previous view-based
techniques use classical 2D shape descriptors such as Fourier invariants,
Zernike moments, Scale Invariant Feature Transform-based local features and 2D
Digital Fourier Transform coefficients. These methods describe each object
independent of others. In this work, we explore data driven subspace models,
such as Principal Component Analysis, Independent Component Analysis and
Nonnegative Matrix Factorization to describe the shape information of the
views. We treat the depth images obtained from various points of the view
sphere as 2D intensity images and train a subspace to extract the inherent
structure of the views within a database. We also show the benefit of
categorizing shapes according to their eigenvalue spread. Both the shape
categorization and data-driven feature set conjectures are tested on the PSB
database and compared with the competitor view-based 3D shape retrieval
algorithmsComment: Three-Dimensional Image Processing (3DIP) and Applications
(Proceedings Volume) Proceedings of SPIE Volume: 7526 Editor(s): Atilla M.
Baskurt ISBN: 9780819479198 Date: 2 February 201
Query by String word spotting based on character bi-gram indexing
In this paper we propose a segmentation-free query by string word spotting
method. Both the documents and query strings are encoded using a recently
proposed word representa- tion that projects images and strings into a common
atribute space based on a pyramidal histogram of characters(PHOC). These
attribute models are learned using linear SVMs over the Fisher Vector
representation of the images along with the PHOC labels of the corresponding
strings. In order to search through the whole page, document regions are
indexed per character bi- gram using a similar attribute representation. On top
of that, we propose an integral image representation of the document using a
simplified version of the attribute model for efficient computation. Finally we
introduce a re-ranking step in order to boost retrieval performance. We show
state-of-the-art results for segmentation-free query by string word spotting in
single-writer and multi-writer standard datasetsComment: To be published in ICDAR201
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