Views selection for SIFT based object modeling and recognition

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper we focus on automatically learning object models in the framework of keypoint based object recognition. The proposed method uses a collection of views of the objects to build the model. For each object the collection is composed of N×M views obtained rotating the object around its vertical and horizontal axis. As keypoint based object recognition using a complete set of views is computationally expensive, we focused on the definition of a selection method that creates, for each object, a subset of the initial views that visually summarize the characteristics of the object and should be suited for recognition. We select the views by determining maxima and minima of a function, based on the number of SIFT descriptors able to evaluate views similarity and relevance. Experimental results for recognition on a publicly available dataset are reported.
Original languageEnglish
Title of host publication2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)
Number of pages5
Publication statusPublished - 2016

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Signal Processing

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