DocBrowse: a system for information retrieval from document image data

1996 ◽  
Author(s):  
Mysore Y. Jaisimha ◽  
Andrew G. Bruce ◽  
Thien Nguyen
Keyword(s):  
Information Retrieval ◽  
Image Data ◽  
Document Image

Music Information Retrieval in P2P Networks

2011 ◽  
pp. 190-216
Author(s):  
Ioannis Karydis
Keyword(s):  
Information Retrieval ◽  
Music Information Retrieval ◽  
Image Data ◽  
Peer To Peer ◽  
P2p Network ◽  
P2p Networks ◽  
Text And Image ◽  
General Terms ◽  
Peer To Peer Networks ◽  
Music Information

In this chapter we present the most significant trends in recent research in the field of content-based music information retrieval in peer-to-peer networks. Despite the diminished attention the area has received in general terms, the relatively close area of metadata MIR in P2P is by far new. As metadata prove to be inefficient for the purposes of MIR as well as the peculiarities of music in comparison to text and image data, developing dedicated solutions for CBMIR in P2P networks becomes a necessity while the challenges faced therein, unique. Depending on the type of P2P network, a number of prominent research works are presented and compared in this chapter.

scholarly journals An Efficient Two-Level Dictionary-Based Technique for Segmentation and Compression Compound Images

2020 ◽  
Vol 14 (4) ◽  
pp. 52
Author(s):  
Nidhal Kamel Taha El-Omari
Keyword(s):  
Data Storage ◽  
Image Data ◽  
Data File ◽  
Document Image ◽  
Relative Reduction ◽  
Storage Space ◽  
Space Reduction ◽  
Long Run ◽  
Block Based ◽  
Real Essence

Image data compression algorithms are essential for getting storage space reduction and, perhaps more importantly, to increase their transfer rates, in terms of space-time complexity. Considering that there isn't any encoder that gives good results across all image types and contents, this paper proposed an evolvable lossless statistical block-based technique for segmentation and compression compound or mixed documents that have different content types, such as pictures, graphics, and/or texts. Derived from the number of detected colors and to achieve better compression ratios, a new well-defined representation of the image is created which nonetheless retains the same image components. With the effort of reducing noise or other variations inside the scanned image, some primary operations are implemented. Thereafter, the proposed algorithm breaks down the compound document image into equal-size-square blocks. Next, inspired by the number of colors detected in each block, these blocks are categorized into a set of six-image objects, called classes, where each one contains a set of closely interrelated pixels that share the same common relevant attributes like color gamut and number, color occurrence, grey level, and others. After that, a new representation of these coherent classes is formed using the Lookup Dictionary Table (LUD), which is the real essence of this proposed algorithm. In order to form distinguishable labeled regions sharing the same attributes, adjacent blocks of similar color features are consolidated together into a single coherent whole entity, called segments or regions. After each region is encoded by one of the most off-the-shelf applicable compression techniques, these regions are eventually fused together into a single data file which then subjects to another compression stage to ensure better compression ratios. After the proposed algorithm has been applied and tested on a database containing 3151 24-bit-RGB-bitmap document images, the empirically-based results prove that the overall algorithm is efficient in the long run and has superior storage space reduction when compared with other existing algorithms. As for the empirical findings, the proposed algorithm has achieved (71.039 %) relative reduction in the data storage space.

scholarly journals An Intelligent Web Digital Image Metadata Service Platform for Social Curation Commerce Environment

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Seong-Yong Hong ◽  
Sung-Joon Lee
Keyword(s):  
Knowledge Management ◽  
Information Retrieval ◽  
Digital Image ◽  
Image Data ◽  
Multimedia Databases ◽  
Multimedia Data ◽  
Shopping Mall ◽  
Global Features ◽  
Image Information ◽  
Image Metadata

Information management includes multimedia data management, knowledge management, collaboration, and agents, all of which are supporting technologies for XML. XML technologies have an impact on multimedia databases as well as collaborative technologies and knowledge management. That is, e-commerce documents are encoded in XML and are gaining much popularity for business-to-business or business-to-consumer transactions. Recently, the internet sites, such as e-commerce sites and shopping mall sites, deal with a lot of image and multimedia information. This paper proposes an intelligent web digital image information retrieval platform, which adopts XML technology for social curation commerce environment. To support object-based content retrieval on product catalog images containing multiple objects, we describe multilevel metadata structures representing the local features, global features, and semantics of image data. To enable semantic-based and content-based retrieval on such image data, we design an XML-Schema for the proposed metadata. We also describe how to automatically transform the retrieval results into the forms suitable for the various user environments, such as web browser or mobile device, using XSLT. The proposed scheme can be utilized to enable efficient e-catalog metadata sharing between systems, and it will contribute to the improvement of the retrieval correctness and the user’s satisfaction on semantic-based web digital image information retrieval.

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