Recognizing Products: A Per-exemplar Multi-label Image Classification Approach

  • Marian George
  • Christian Floerkemeier
Conference paper

DOI: 10.1007/978-3-319-10605-2_29

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)
Cite this paper as:
George M., Floerkemeier C. (2014) Recognizing Products: A Per-exemplar Multi-label Image Classification Approach. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8690. Springer, Cham

Abstract

Large-scale instance-level image retrieval aims at retrieving specific instances of objects or scenes. Simultaneously retrieving multiple objects in a test image adds to the difficulty of the problem, especially if the objects are visually similar. This paper presents an efficient approach for per-exemplar multi-label image classification, which targets the recognition and localization of products in retail store images. We achieve runtime efficiency through the use of discriminative random forests, deformable dense pixel matching and genetic algorithm optimization. Cross-dataset recognition is performed, where our training images are taken in ideal conditions with only one single training image per product label, while the evaluation set is taken using a mobile phone in real-life scenarios in completely different conditions. In addition, we provide a large novel dataset and labeling tools for products image search, to motivate further research efforts on multi-label retail products image classification. The proposed approach achieves promising results in terms of both accuracy and runtime efficiency on 680 annotated images of our dataset, and 885 test images of GroZi-120 dataset. We make our dataset of 8350 different product images and the 680 test images from retail stores with complete annotations available to the wider community.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Marian George
    • 1
  • Christian Floerkemeier
    • 1
  1. 1.Department of Computer ScienceETH ZurichSwitzerland

Personalised recommendations