1,504 research outputs found
Extraction of Projection Profile, Run-Histogram and Entropy Features Straight from Run-Length Compressed Text-Documents
Document Image Analysis, like any Digital Image Analysis requires
identification and extraction of proper features, which are generally extracted
from uncompressed images, though in reality images are made available in
compressed form for the reasons such as transmission and storage efficiency.
However, this implies that the compressed image should be decompressed, which
indents additional computing resources. This limitation induces the motivation
to research in extracting features directly from the compressed image. In this
research, we propose to extract essential features such as projection profile,
run-histogram and entropy for text document analysis directly from run-length
compressed text-documents. The experimentation illustrates that features are
extracted directly from the compressed image without going through the stage of
decompression, because of which the computing time is reduced. The feature
values so extracted are exactly identical to those extracted from uncompressed
images.Comment: Published by IEEE in Proceedings of ACPR-2013. arXiv admin note: text
overlap with arXiv:1403.778
Compressive Mining: Fast and Optimal Data Mining in the Compressed Domain
Real-world data typically contain repeated and periodic patterns. This
suggests that they can be effectively represented and compressed using only a
few coefficients of an appropriate basis (e.g., Fourier, Wavelets, etc.).
However, distance estimation when the data are represented using different sets
of coefficients is still a largely unexplored area. This work studies the
optimization problems related to obtaining the \emph{tightest} lower/upper
bound on Euclidean distances when each data object is potentially compressed
using a different set of orthonormal coefficients. Our technique leads to
tighter distance estimates, which translates into more accurate search,
learning and mining operations \textit{directly} in the compressed domain.
We formulate the problem of estimating lower/upper distance bounds as an
optimization problem. We establish the properties of optimal solutions, and
leverage the theoretical analysis to develop a fast algorithm to obtain an
\emph{exact} solution to the problem. The suggested solution provides the
tightest estimation of the -norm or the correlation. We show that typical
data-analysis operations, such as k-NN search or k-Means clustering, can
operate more accurately using the proposed compression and distance
reconstruction technique. We compare it with many other prevalent compression
and reconstruction techniques, including random projections and PCA-based
techniques. We highlight a surprising result, namely that when the data are
highly sparse in some basis, our technique may even outperform PCA-based
compression.
The contributions of this work are generic as our methodology is applicable
to any sequential or high-dimensional data as well as to any orthogonal data
transformation used for the underlying data compression scheme.Comment: 25 pages, 20 figures, accepted in VLD
DWT-CompCNN: Deep Image Classification Network for High Throughput JPEG 2000 Compressed Documents
For any digital application with document images such as retrieval, the
classification of document images becomes an essential stage. Conventionally
for the purpose, the full versions of the documents, that is the uncompressed
document images make the input dataset, which poses a threat due to the big
volume required to accommodate the full versions of the documents. Therefore,
it would be novel, if the same classification task could be accomplished
directly (with some partial decompression) with the compressed representation
of documents in order to make the whole process computationally more efficient.
In this research work, a novel deep learning model, DWT CompCNN is proposed for
classification of documents that are compressed using High Throughput JPEG 2000
(HTJ2K) algorithm. The proposed DWT-CompCNN comprises of five convolutional
layers with filter sizes of 16, 32, 64, 128, and 256 consecutively for each
increasing layer to improve learning from the wavelet coefficients extracted
from the compressed images. Experiments are performed on two benchmark
datasets- Tobacco-3482 and RVL-CDIP, which demonstrate that the proposed model
is time and space efficient, and also achieves a better classification accuracy
in compressed domain.Comment: In Springer Journal - Pattern Analysis and Applications under Minor
Revisio
Entropy Computation of Document Images in Run-Length Compressed Domain
Compression of documents, images, audios and videos have been traditionally
practiced to increase the efficiency of data storage and transfer. However, in
order to process or carry out any analytical computations, decompression has
become an unavoidable pre-requisite. In this research work, we have attempted
to compute the entropy, which is an important document analytic directly from
the compressed documents. We use Conventional Entropy Quantifier (CEQ) and
Spatial Entropy Quantifiers (SEQ) for entropy computations [1]. The entropies
obtained are useful in applications like establishing equivalence, word
spotting and document retrieval. Experiments have been performed with all the
data sets of [1], at character, word and line levels taking compressed
documents in run-length compressed domain. The algorithms developed are
computational and space efficient, and results obtained match 100% with the
results reported in [1].Comment: Published in IEEE Proceedings 2014 Fifth International Conference on
Signals and Image Processin
Indexing, browsing and searching of digital video
Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver
Shift Aggregate Extract Networks
We introduce an architecture based on deep hierarchical decompositions to
learn effective representations of large graphs. Our framework extends classic
R-decompositions used in kernel methods, enabling nested "part-of-part"
relations. Unlike recursive neural networks, which unroll a template on input
graphs directly, we unroll a neural network template over the decomposition
hierarchy, allowing us to deal with the high degree variability that typically
characterize social network graphs. Deep hierarchical decompositions are also
amenable to domain compression, a technique that reduces both space and time
complexity by exploiting symmetries. We show empirically that our approach is
competitive with current state-of-the-art graph classification methods,
particularly when dealing with social network datasets
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