computer vision group department of computer science university of illinois at urbana-champaign
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Multimodal Information Access & Synthesis Abstract Process Demo Results Future Work Questions OutlineTRANSCRIPT
Computer Vision GroupDepartment of Computer Science
University of Illinois at Urbana-Champaign
Multimodal Information Access & Synthesis
Sang Hyun ParkJoel QuintanaRobert Rand
David Forsyth
Recognition and Efficient Retrieval of Recognition and Efficient Retrieval of Similar Images in Large Datasets Similar Images in Large Datasets
Using Visual WordsUsing Visual Words
Multimodal Information Access & Synthesis
Abstract Process Demo Results Future Work Questions
OutlineOutline
Multimodal Information Access & Synthesis
Problem: We want to...Identify pictures by content rather than color.Compare large sets of images to find near duplicates.Recognize similar pictures despite small changes.
Challenges: Similar Images can be...in different filenames.in different formats.in different sizes and arrangements.stretched, skewed, colored and otherwise altered.
AbstractAbstract
Multimodal Information Access & Synthesis
Why would we want to find near duplicate images?News ReportsForged PhotosSocial NetworksPicture/Mugshot MatchingWeapon & Symbol ID
ApplicationApplication
Multimodal Information Access & Synthesis
ProcessProcessImages Database
Part1: Get the Visual Words
Extract SIFT Features
Interest Points Database
Group Similar Interest Points (Kmeans)
List of General Points (Visual Words)
Multimodal Information Access & Synthesis
ProcessProcessOriginal Images
Database
Part2: Store Histograms of Visual Words of the Images on the Database
Image
Add Histogram to the Histograms Database Histograms Database
Calculate Histogram of Visual Words
Histogram of Visual Words
Multimodal Information Access & Synthesis
ProcessProcessPart3: Retrieval of Similar Images
New Image
Histograms Database
Calculate Histogram of Visual Words
Histogram of Visual Words
Query For Nearest Neighbors
List Of Nearest Neighbors
Multimodal Information Access & Synthesis
DemoDemo
Multimodal Information Access & Synthesis
Current Configuration– 10 Interest points per image– 3000 Visual words (K-mean Clustering)– KDTree to get approximate nearest neighbors– Precision : ~0.50
Future Configuration– All Interest points from images– More Clustering Algorithms (Hierarchical K-means / KDTree)– Usage of Full Potential of FLANN
ResultsResults
Multimodal Information Access & Synthesis
Bigger Database– Web (Image Search Engine / Flicker / Facebook)
Multiple Queries
– Parallelized Processing (Efficient Processing of Queries)
Other Application– Detection of objects inside images: logos, symbols, tattoos,
weapons, etc– Finding relationships between people according to their common
pictures on social networks
Future WorkFuture Work
Multimodal Information Access & Synthesis
QuestionsQuestions