CrowdSearch: Exploiting Crowds for Accurate Real-Time Image Search on Mobile Phones Original work by Yan, Kumar & Ganesan Presented by Tim Calloway

Roadmap  Problem Description  What is “crowdsourcing”?  System Architecture  The CrowdSearch Algorithms  Delay Prediction  Validation Prediction  Experimental Evaluation  Discussion/Criticism  Questions

The Perceived Problem  Text-based search is easy…

The Perceived Problem  Mobile-based search will become more important in the future.  More than 70% of smart phone users perform searches.  Expected to be more mobile searches than non-mobile searches soon  Text-based mobile searches are easy as well…

The Perceived Problem  But how does a mobile phone user search for this?  No visible words/letters; too far away to know the address.

The Perceived Problem  Ways to find out what that building is:  Ask random people on the street  Travel to the building to see the address/sign  Take a picture of the building with your mobile device and send to a search engine…  How easy is image searching on a mobile phone though?

The Perceived Problem  Image search is a non-trivial problem – have to deal with variations in lighting, texture, image quality, etc.  Even when results are returned, scrolling through multiple pages on a mobile device is cumbersome.  Search should be precise and return very few erroneous results.  Multimedia searches require significant  Memory  Storage  Computing resources

The Proposed Solution  CrowdSearch – Attempts to provide an accurate, image search system for mobile devices by combining…  Automated image search and  Real-time human validation of search results  Leverage crowdsourcing through Amazon Mechanical Turk (AMT)

The Proposed Solution  Humans are good at comparing images  Could an automated search determine these two images are of the same building?  Crowdsourcing increases search result accuracy.

Balancing Tradeoffs  Result delay  Should minimize delay or at least keep it within a user-provided bound  Result accuracy  Strive for high (i.e., ≥ 95%) accuracy  Monetary cost  Low cost is better than high cost  Energy  Should consume minimal battery power

System Architecture

 Three main components:  Mobile Device  Initiates queries  Displays responses  Performs local image processing (maybe)  Remote Server  Performs automated image search  Triggers image validation tasks  Crowdsourcing System (AMT)  Validates image search results

System Operation Overview

 How do we minimize delay and cost while maximizing accuracy?

Accuracy Considerations  How many validations are required for 95% accuracy?  Requiring at least three validations out of five achieves ≥ 95% accuracy.

Optimizing Delay  Utilize parallel posting  Post all candidate images to the crowdsourcing system at the same time.  But this approach increases cost! 5 cents = 20 cents 5 cents

Optimizing Cost  Utilize serial posting  Post top-ranked candidate first, wait for responses, then post next candidate if necessary.  This approach increases delay!

CrowdSearch Delay/Cost Optimization  Combine elements of parallel and serial posting  Prediction requires delay and validation models  Goal: want at least one verified result by the deadline.

CrowdSearch Delay/Cost Optimization

Delay Prediction Model  The delay of a single response is the combination of acceptance delay and submission delay.  Both of these follow an exponential distribution with an offset.  Thus, overall delay is the convolution of these delays.

Delay Prediction Model Performance

Validation Model  Given a response set S, want to compute probability of positive validation result.  Use training data to set these probabilities  If the probability of a positive result is less than some threshold, send the next candidate to validation.  In this example, if the threshold were set to < 76%, the server would post the next candidate image to AMT.

Power Considerations  Should some image processing occur on the local device or should it be outsourced to the server?  It depends!  Use remote processing when WiFi is available.  Use local processing when only 3G is available

Experimental Results  Any of the crowdsourcing schemes lead to better results!  Some types of images are easier for automated searches to handle than others

Experimental Results  CrowdSearch leads to (given a long enough deadline)…  Behavior close to parallel posting for recall  Behavior close to serial posting for search cost

Thoughts/Criticism  The limited nature of the solution  Limitation to the four categories  Buildings  Books  Flowers  Faces  Only 1000 images in the backend database.  Would increasing the number of automated search images increase total task time in a significant way?

Thoughts/Criticism  How useful is this anyway?  Are people willing to go through the trouble to set up a payment account and pay 5-20 cents for a search?  How much effort would it usually take for someone to find out what the object is through traditional means?  Especially for books!  Privacy concerns  People utilizing CrowdSearch must accept the fact that random strangers know what they are looking at and searching for.  Additionally, their GPS information might be provided to the CrowdSearch servers.  What about the privacy of the object of the search?  Undercover police officers

Questions?