89,832 research outputs found
Block-based Classification Method for Computer Screen Image Compression
In this paper, a high accuracy and reduced processing time block based classification method for computer screen images is presented. This method classifies blocks into five types: smooth, sparse, fuzzy, text and picture blocks. In a computer screen compression application, the choice of block compression algorithm is made based on these block types. The classification method presented has four novel features. The first novel feature is a combination of Discrete Wavelet Transform (DWT) and colour counting classification methods. Both of these methods have only been used for computer image compression in isolation in previous publications but this paper shows that combined together more accurate results are obtained overall. The second novel feature is the classification of the image blocks into five block types. The addition of the fuzzy and sparse block types make the use of optimum compression methods possible for these blocks. The third novel feature is block type prediction. The prediction algorithm is applied to a current block when the blocks on the top and the left of the current block are text blocks or smooth blocks. This new algorithm is designed to exploit the correlation of adjacent blocks and reduces the overall classification processing time by 33%. The fourth novel feature is down sampling of the pixels in each block which reduces the classification processing time by 62%. When both block prediction and down sampling are enabled, the classification time is reduced by 74% overall. The overall classification accuracy is 98.46%
Textual Case-based Reasoning for Spam Filtering: a Comparison of Feature-based and Feature-free Approaches
Spam filtering is a text classification task to which Case-Based Reasoning (CBR) has been successfully applied. We describe the ECUE system, which classifies emails using a feature-based form of textual CBR. Then, we describe an alternative way to compute the distances between cases in a feature-free fashion, using a distance measure based on text compression. This distance measure has the advantages of having no set-up costs and being resilient to concept drift. We report an empirical comparison, which shows the feature-free approach to be more accurate than the feature-based system. These results are fairly robust over different compression algorithms in that we find that the accuracy when using a Lempel-Ziv compressor (GZip) is approximately the same as when using a statistical compressor (PPM). We note, however, that the feature-free systems take much longer to classify emails than the feature-based system. Improvements in the classification time of both kinds of systems can be obtained by applying case base editing algorithms, which aim to remove noisy and redundant cases from a case base while maintaining, or even improving, generalisation accuracy. We report empirical results using the Competence-Based Editing (CBE) technique. We show that CBE removes more cases when we use the distance measure based on text compression (without significant changes in generalisation accuracy) than it does when we use the feature-based approach
Application of compression-based distance measures to protein sequence classification: a methodological study
Abstract
Motivation: Distance measures built on the notion of text compression have been used for the comparison and classification of entire genomes and mitochondrial genomes. The present study was undertaken in order to explore their utility in the classification of protein sequences.
Results: We constructed compression-based distance measures (CBMs) using the Lempel-Zlv and the PPMZ compression algorithms and compared their performance with that of the Smith–Waterman algorithm and BLAST, using nearest neighbour or support vector machine classification schemes. The datasets included a subset of the SCOP protein structure database to test distant protein similarities, a 3-phosphoglycerate-kinase sequences selected from archaean, bacterial and eukaryotic species as well as low and high-complexity sequence segments of the human proteome, CBMs values show a dependence on the length and the complexity of the sequences compared. In classification tasks CBMs performed especially well on distantly related proteins where the performance of a combined measure, constructed from a CBM and a BLAST score, approached or even slightly exceeded that of the Smith–Waterman algorithm and two hidden Markov model-based algorithms.
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Supplementary information
Artificial Sequences and Complexity Measures
In this paper we exploit concepts of information theory to address the
fundamental problem of identifying and defining the most suitable tools to
extract, in a automatic and agnostic way, information from a generic string of
characters. We introduce in particular a class of methods which use in a
crucial way data compression techniques in order to define a measure of
remoteness and distance between pairs of sequences of characters (e.g. texts)
based on their relative information content. We also discuss in detail how
specific features of data compression techniques could be used to introduce the
notion of dictionary of a given sequence and of Artificial Text and we show how
these new tools can be used for information extraction purposes. We point out
the versatility and generality of our method that applies to any kind of
corpora of character strings independently of the type of coding behind them.
We consider as a case study linguistic motivated problems and we present
results for automatic language recognition, authorship attribution and self
consistent-classification.Comment: Revised version, with major changes, of previous "Data Compression
approach to Information Extraction and Classification" by A. Baronchelli and
V. Loreto. 15 pages; 5 figure
Towards the text compression based feature extraction in high impedance fault detection
High impedance faults of medium voltage overhead lines with covered conductors can be identified by the presence of partial discharges. Despite it is a subject of research for more than 60 years, online partial discharges detection is always a challenge, especially in environment with heavy background noise. In this paper, a new approach for partial discharge pattern recognition is presented. All results were obtained on data, acquired from real 22 kV medium voltage overhead power line with covered conductors. The proposed method is based on a text compression algorithm and it serves as a signal similarity estimation, applied for the first time on partial discharge pattern. Its relevancy is examined by three different variations of classification model. The improvement gained on an already deployed model proves its quality.Web of Science1211art. no. 214
Learning Low-Rank Representations for Model Compression
Vector Quantization (VQ) is an appealing model compression method to obtain a
tiny model with less accuracy loss. While methods to obtain better codebooks
and codes under fixed clustering dimensionality have been extensively studied,
optimizations of the vectors in favour of clustering performance are not
carefully considered, especially via the reduction of vector dimensionality.
This paper reports our recent progress on the combination of dimensionality
compression and vector quantization, proposing a Low-Rank Representation Vector
Quantization () method that outperforms previous VQ
algorithms in various tasks and architectures. joins
low-rank representation with subvector clustering to construct a new kind of
building block that is directly optimized through end-to-end training over the
task loss. Our proposed design pattern introduces three hyper-parameters, the
number of clusters , the size of subvectors and the clustering
dimensionality . In our method, the compression ratio could be
directly controlled by , and the final accuracy is solely determined by
. We recognize as a trade-off between low-rank
approximation error and clustering error and carry out both theoretical
analysis and experimental observations that empower the estimation of the
proper before fine-tunning. With a proper , we evaluate
with ResNet-18/ResNet-50 on ImageNet classification
datasets, achieving 2.8\%/1.0\% top-1 accuracy improvements over the current
state-of-the-art VQ-based compression algorithms with 43/31
compression factor
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