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Extending Google Goggles to Identify services offered by objects CMSC 601-Basic Research Skills

This presentation explores the enhancement of Google Goggles by integrating cloud computing to identify services associated with objects. Mobile technology allows information access anytime, but traditional mobile devices struggle with processing power and display limitations. By offloading computation to the cloud, we can increase efficiency and scalability, reduce mobile power consumption, and improve the performance of visual search. The proposed architecture combines image-based searches with geographic information, enabling smartphones to retrieve and offer meaningful services about scanned objects.

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Extending Google Goggles to Identify services offered by objects CMSC 601-Basic Research Skills

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  1. Extending Google Goggles to Identify services offered by objectsCMSC 601-Basic Research Skills May 11, 2011 Presented by, AnuragKorde

  2. Introduction • Mobile technology- access information from anywhere at anytime • Cloud computing-does computations on shared resources and provides storage, security and infrastructure as a service[3] • Semantic computing-stores, analyzes and find correlation between data[4]

  3. Problems • Mobile devices have limited power and processing capabilities • Increase in applications requiring high computation • Small display- difficult to type search queries • Google Goggles[1] do not give services offered by objects • Large computation required for image analysis and comparison

  4. Solution and its Impact • Solution • Reduce volume of processing on mobile • Offload the computation on cloud • Send only results to mobile • Use visual search along with geo-location information • Importance • Using Cloud computing increases scalability and efficiency, low cost, high performance • Reduces mobile power consumption • Increase in number of applications and its use • Reduces image comparison with large image dataset

  5. Related Work-I • Google Goggles[1] • Allows visual search • No need to type or say query • Google has a dataset of 1 billion images Images:[1]

  6. Related Work-II • Google Sky Map[2] • Point your mobile towards sky • Shows planets, stars, constellations • Uses mobile geo-location and compass angle Images:[2]

  7. Related Work-III • Strengths • User friendly • Works on books, landmarks, logos, business cards, etc • Solves Sudoku!! • Weaknesses • Cannot identify common objects: animals, plants, food • Cannot solve complex AI games • Gives only the information associated with object • Searches over a large image dataset

  8. Proposed Architecture Take a snap Results(Information / Services) Image Image search and service mapping Images:[1] and www.google.com/images

  9. Implementation/Methodology • Approach-I • Image based • Search and compare image with large dataset • Filter the search based on context • Extract service information • Approach-II • Uses geographic location of mobile • Extract information using GPS, accelerometer and compass • Find the intersection between the data and map • Extract service and other information Images: www.google.com/images

  10. Evaluation • Develop and deploy a working prototype • Run application in: • Various scenarios • With different test objects • Calculate error rate • Improve dataset if necessary

  11. Future Work • Improve and expand the image dataset • Identify common objects: animals, plants, food • Play difficult AI games like chess • Face recognition using social profile

  12. Conclusion • Google Goggles[1]-provides visual search on limited objects, needs one extension i.e. give services by object • New approach for visual searching-location and context based • Added new functionality i.e. retrieval of services offered by the captured object

  13. References [1] www.google.com/mobile/goggles [2] http://www.google.com/mobile/skymap [3] SrinivasaRao V, NageswaraRao N K, and E Kusuma Kumari. Cloud computing: An overview. Journal of Theoretical and Applied Information Technology. [4] SonalAnand, Sarvesh Gupta, ShwetaFatnani, Varsha Sharma, and Deepti Jain. Semantic cloud for mobile technology. International Journal of Computer Applications

  14. Thank You….!!!

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