Post on 24-Feb-2016
description
MediaScope: Selective On-Demand Media Retrieval from Mobile Devices
Yurong Jiang, Xing Xu, Peter Terlecky, Tarek Abdelzaher, Amotz Bar-Noy, Ramesh Govindan
1IPSN 2013
Availability Gap 2
Trends in Photo and Video Uploads – More & More Delayed• increasing bandwidth requirements
• high-resolution cameras• restricted usage limits for cellular data plan
• restricted cellular capacityDelayed Uploads Resulting in – Availability Gap
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Availability Gap in 3
Study of Uploads• 40 users• 50 images/user
50% Images are Uploaded 10+ Days Later! – Not Recent!
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0 6 12 18 24 30 36 42 48 540
0.5
1
Availability Gap (Days)
%
Bridge the Availability Gap ? 4
Mall Robbery Sporting Event
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Bridge the Availability Gap 5
Distributed Image Database: Most Recent, Most Diverse
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MediaScope: Timely On-Demand Media Retrieval 6
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On-Demand Retrieval Bridges the Availability Gap
Timely Retrieval Can be Important for Many Apps
Images from today’s game…
Respond in 30 seconds…
MediaScope Approach 7
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NBA USA
MediaScope Queries – for Different Needs8Top-K
• Find K most Similar Images to Target Image
Spanner
• Find a Collection of most Dissimilar Images
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Challenges and Contributions 9
Mediascope – Timely On-Demand Image Retrieval
Selecting Relevant Query Results
• Challenge: image search computationally-expensive• Contribution: adapt & generalize image-search
technique
Return Results in a Timely Manner
• Challenge: variable and limited wireless bandwidth• Contribution: optimize information-content uploaded
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Img3 = { Loc = {Philly, USA}, Time = 2013-0408T09:00:00},Visual Feature = {0, 7, 0, 3, … }}
Img2 = { Loc = {L.A., USA}, Time = 2013-0316T10:00:00},Visual Feature = {1, 2, 0, 4, … }}
Img1 = { Loc = {Beijing, China}, Time = 2012-0716T19:20:30},Visual Feature = {0, 3, 2, 5, … }}
Image Search – on Image Feature Space 10
Location & Time – Filtering Out Irrelevant Media Files
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Feature Vectors for Images – Support Geometric Queries
Visual Feature = {0, 3, 2, 5, 6, … }
• statistical summary• color histogram
Similarity Metric: Sim(x1, x2)
• n coefficients feature vector• a point in n-dimensional space
• Sim(x1, x2) ~ Distance(x1, x2)
Similarity on Feature Vectors 11
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Medusa
MediaScope System Overview
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MSCloud
MSCloudQMSCloudDB
MSMobile
ObjectUploader
FeatureExtractor
MSMobile - Feature Extractor 13
CEDD – Color & Edge Directivity Descriptor• 144 coefficients ranging from [0..7]• 54 bytes / image
Image Resizing before Feature Extraction• short feature-extraction time• acceptable error rate
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Erro
r R
ate
(%)
1632x1224
1280x960 1024x768 960x720 816x612500
1500
2500
3500
4500
Resolution
Tim
e (m
s)
1632x1224
1280x960 1024x768 960x720 816x612500
1500
2500
3500
4500
0510152025
Resolution
Tim
e (m
s)
1.5s proc. time
4% error rate
Geometric Queries in MediaScope 14
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Top-K Spanner Cluster Representative
max(Sim(x, query)) min(max(Sim(x1, x2))
Clusteringmax(Sim(x, center))
O(V logV) O(V2) O(IV)
Similar x1, x2 Greater Sim(x1, x2) ValueIn Particular, Sim(x, x) = ∞
MSCloud – Timely Retrieval for Concurrent Queries15
Trading off Query Completeness for Timeliness
• maximize amount of retrieved information• credit-assignment mechanism
Q1 Q1
Q1 Q1 Q1 P1
P2
Q1
Q2 Q2 Q2
Q2 Q2 Q2 Q2
Q2
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1000
MSCloud – Queries & Credit Assignment 16
Each Query is Assigned Credits• divide up credits among selected images by importance
Spanner
• credit ∝ similarity to target image
• (credit)-1 ∝ average similarity to other images
Top-K
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Credit Assignment ~ Feature Space Geometry
800 100 100
MSCloud – Credit Based Scheduling 17
From P1From P2
From Q1
From Q2
Optimization Goal: Maximize Uploaded Credits
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MSCloud – Credit Based Scheduling 18
Q1 Q1
Q1 Q1 Q1P1
P2Q1 1. Filesize
2. Credit
3. Deadline
MSCloud
MSMobile
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P1
MSCloud – Credit Based Scheduling @ Phone 19
{(Filesize, Credit, Deadline)}• max(uploaded credits on-time)
Optimal Scheduling for Same File Size (Same Uploading Time)• arrange images by deadlines• always give up smallest credit object
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MediaScope Evaluation 20
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Prototype
MSCloud
• MSCloudQ (4300 LOC)• PHP, Python
• MSCloudDB• MySql
MSMobile
• Java (1100 LOC)
Evaluation
Setup• MSCloud - Dell XPS 7100• MSMobile - 8 Android
PhonesMetric• Query Completeness
Methodology• Query Trace Replay
Query Completeness 21
MCF: Max Credit First
EDF: Earliest Deadline First
RR: Round Robin
OMNI: Omniscient
MSC: MediaScope Credit-Based
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MediaScope Overhead 22
Communication & system Overhead Average Latency (ms)
C2DM Message 150Feature Vector Download 138Query Result Response 54
Upload Scheduling 46Query Parsing 24
Component overhead Energy (µAh)Feature Extraction 331
Low Latency!
10% Battery Consumption ~ 400+ images
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MediaScope Summary 23
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MediaScope: Timely On-Demand Media Retrieval
• accurately & efficiently extracts visual features
• supports geometric queries over feature space
• timely returns informative retrieval results for queries
Bridges Availability Gap
24Query Sample
Query Completeness 25
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MediaScope – Related Work 26
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Content Based Image Retrieval• Faced Image Search, Virage Image Search Engine,
ImgSeek• Search on Local Database vs. Mobile Setting
Image Search on Mobile Devices• CrowdSearch• Centralized Database vs. Distributed Database from
Mobile Device
General Image Search Problem• Similarity Match (k-NN with k=1)• Geometric Search Queries on Feature Space