Skip to main content
SpringerLink
Log in

Object search using object co-occurrence relations derived from web content mining

  • Original Research
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

We present the novel framework of knowledge construction (ICC: Independent Co-occurring based Construction) based on co-occurrence relations of objects. We compare its characteristics with that of general approach (DCC: Dependent Co-occurring based Construction) in various construction aspects: variations of trained probability values, percentage differences (probability value and priority ranking order), and reconstruction time. The similarity of their data content and faster reconstruction time of ICC suggest that ICC is more suitable for applications of service robot. Instead of using visual feature, we employed annotated data, such as word-tagging images, as the training set to increase the accuracy of correspondence between related keywords and images. The task of object search in unknown environment is selected to evaluate the applicability of using constructed knowledge (OCR: Object Co-occurrence Relations). We explore the search behaviors, provided by OCR-based search (indirect search) and greedy search (direct search), in simulation experiments with five different starting robot positions. Their search behaviors are also compared from the aspects of consumed computational time, travel distance, and number of visited locations. The certainty of success of OCR-based search assures us of its benefit. Moreover, the object search experiment in unknown human environment is conducted by a mobile robot, equipped with a stereo camera, to show the possibility of using OCR in the search in real world.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Wixson Lambert E, Ballard Dana H (1994) Using intermediate objects to improve the efficiency of visual search. Int J Comput Vis 12:209–230

    Article  Google Scholar 

  2. Meger D, Forss’en P-E, Lai K, Helmer S, McCann S, Southey T, Baumann M, Little JJ, Lowe DG, Dow B (2008) Curious george: an attentive semantic robot. Robotics Auton Syst 56:503–511

    Article  Google Scholar 

  3. Masuzawa H, Miura J (2009) Observation planning for efficient environment information summarization. In: IEEE/RSJ international conference on intelligent robots and system, pp 5794–5800

  4. Ma J, Burdick JW (2010) A probabilistic framework for stereo-vision based 3D object search with 6D pose estimation. In: IEEE international conference on robotics and automation, pp 2036–2042

  5. Sjöö K, Gálvez-López D, Paul C, Jensfelt P, Kragic D (2009) Object search and localization for indoor mobile robot. J Comput Inform Technol 17:67–80

    Google Scholar 

  6. Galindo Cipriano, Fernandez-Madrigal Juan-Antonio, Gonzalez Javier, Saffiotti Alessandro (2008) Robot task planning using semantic maps. Robotics Auton Syst 56:955–966

    Article  Google Scholar 

  7. Joho D, Burgard W (2010) Searching for objects: combining multiple cues to object locations using a maximum entropy model. In: IEEE international conference on robotics and automation, pp 723–728

  8. Aydemir A, Sjöö K, Folkesson J, Pronobis A, Jensfelt P (2011) Search in the real World: active visual object search based on spatial relations. In: IEEE international conference on robotics and automation, pp 2818–2824

  9. Shubina K, Tsotsos JK (2010) Visual search for an object in a 3D environment using a mobile robot. Comput Vis Image Underst 114:535–547

    Article  Google Scholar 

  10. Galleguillos C, Rabinovich A, Belongie S (2008) Object categorization using co-occurrence, location and appearance. In: IEEE conference in computer vision and pattern recognition, pp 1–8

  11. Heesoo M, Ju CY, Kyoung ML (2012) Learning object relations via graph-based context model. In: IEEE conference in computer vision and pattern recognition, pp 2727–2734

  12. Kollar T, Roy N (2009) Utilize object-object and object-scene context when planning to find things. In: IEEE international conference on robotics and automation, pp 4116–4121

  13. Chumtong P, Mae Y, Ohara K, Takubo T, Arai T (2011) Object co-occurrence graph for object search in 3D environment. In: International symposium on artificial life and robotics, pp 999–1002

  14. Chumtong P, Mae Y, Ohara K, Takubo T, Arai T (2011) Vision-based object search in unknown human environment using object co-occurrence graph. In: IEEE international conference on robotics and biomimetics (ROBIO), pp 2043–2048

  15. Fergus R, Fei-Fei L, Perona P, Zisserman A (2005) Learning object categories from Google’s image search. In: IEEE international conference on computer vision, vol 2, pp 1816–1823

  16. Thrun S, Burgard W, Fox D (2005) Probabilistic robotics. The MIT Press, Cambridge

    MATH  Google Scholar 

  17. Elfes A (1989) Using occupancy grids for mobile robot perception and navigation. Computer 22:46–57

    Google Scholar 

  18. Andert F (2009) Drawing stereo disparity images into occupancy grids: measurement model and fast implementation. In: IEEE/RSJ international conference on intelligent robots and systems, pp 5191–5197

  19. Ziegler L, Siepmann F, Kortkamp M, Wachsmuth S (2010) Towards an informed search behavior for domestic robots. In: International conference on simulation, modeling, and programming for autonomous robots, pp 241–250

  20. Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59:167–181

    Article  Google Scholar 

  21. Lowe DG (2001) Local feature view clustering for 3D object recognition. Comput Vis Pattern Recogn 1:682–688

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, 1-3, Machikaneyama-cho, Osaka, Toyonaka, 560-8531, Japan

    Puwanan Chumtong, Yasushi Mae & Tatsuo Arai

  2. Department of Mechatronics Engineering, Faculty of Science and Technology, Meijo University, 1-501, Shiogamaguchi, Tempaku, Nagoya, Aichi, 468-8502, Japan

    Kenichi Ohara

  3. Graduate School of Engineering Physical Electronics and Informatics (Information and Communication Engineering), Osaka City University, 3-3-138, Sugimoto, Sumiyoshi, Osaka, 558-8585, Japan

    Tomohito Takubo

Authors
  1. Puwanan Chumtong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yasushi Mae
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kenichi Ohara
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tomohito Takubo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tatsuo Arai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Puwanan Chumtong.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chumtong, P., Mae, Y., Ohara, K. et al. Object search using object co-occurrence relations derived from web content mining. Intel Serv Robotics 7, 1–13 (2014). https://doi.org/10.1007/s11370-013-0139-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-013-0139-1

Keywords

Search

Navigation