Edit Tool: Matchbox Tool

To edit this page, please answer the question that appears below (more info):

What short name does OAIS use for an information package that is used for archiving?

Warning: You are not logged in. Your IP address will be publicly visible if you make any edits. If you log in or create an account, your edits will be attributed to your username, along with other benefits.

Image: In another tab, use the "Upload file" link to upload a logo image. Paste the name of the image here:
Purpose* A single sentence describing the function of the tool. Keep it factual and concise:	Matchbox: Duplicate detection tool for digital document collections.
Homepage The URL of the homepage:
Sourcecode The URL for the source code:
License:
Cost:
Platforms:
Language:
Wikidata ID:
Input formats Start typing and select from the available list. Only add a new format if necessary:
Output Formats: Start typing and select from the available list. Only add a new format if necessary:
Function*: Start typing and select one or more functions from the available list. Only add a new function if absolutely necessary:
Content: Start typing and select one or more content types from the available list. Only add a new content type if absolutely necessary:

OpenHub ID:
Mailing_lists:
Releases_rss:
Issues_rss:

Free text:

= Description = The Matchbox tool is responsible for finding duplicatre pairs in a collection of digital documents based on SIFT features and SSIM methods. Consequently the tool takes a collection path with associated parameters as input. Currently three scenarios are implemented. These are: * Duplicate search in one turn (parameter 'all') * Professional duplicate search (experienced user can execute particular step in 'FindDuplicates' workflow) * Quick check if two documents are duplicates (based on previous BoW dictionary). Further parameters that influence and adjust duplicate analysis are currently investigated. Image processing method: The image processing algorithm can be described in 4 steps: 1. Document feature extraction * Interest point detection (applying Scale Invariant Feature Transform (SIFT) keypoint extraction) * Derivation of local feature descriptors (invariant to geometrical or radiometrical distortions) 2. Learning visual dictionary * Clustering method applied to all SIFT descriptors of all images using k-means algorithm * Run over collection and collect local descriptors in a visual dictionary using Bag-Of-Words (BoW) algorithm 3. Create visual histogram for each image document 4. Detect similar images based on visual histogram and local descriptors. Evaluate similarity score – pair-wise comparison of corresponding keyword frequency histograms for all documents. Conduct structural similarity analysis applying Sturctural SIMilarity (SSIM) approach (1 means identical and 0 means very different) * Rotate * Scale * Mask * Overlaying Usage: FindDuplicates script can be invoked from command line. For standard usage two parameters are required: path to the collection documents and 'all'. scape/pc-qa-matchbox/Python# python2.7 FindDuplicates.py h usage: FindDuplicates.py [-h] [\--threads THREADS] [\--sdk SDK] [\--precluster PRECLUSTER] [\--clahe CLAHE] [\--config CONFIG] [\--featdir FEATDIR] [\--bowsize BOWSIZE] [\--csv] [-v] dir all,extract,compare,train,bowhist,clean = User Experiences = currently installed at Austrian National Library = Development Activity =

Summary:

Cancel

Edit Tool: Matchbox Tool

Navigation menu

Search