Learning Informative SIFT Descriptors for Attentive Object Recognition

Christin Seifert, Gerald Fritz, Lucas Paletta, Horst Bischof

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


With the emerging sensor technologies in mobile devices, such as camera\nphones, visual interpretation methodologies are challenged to provide solutions within the everydays outdoor urban environment. For this purpose, we propose to apply the 'Informative Descriptor Approach' on the SIFT descriptor [4], in order to define the informative SIFT (i-SIFT) descriptor. By attentive matching of i-SIFT keypoints, we provide an innovative method on object detection that significantly improves SIFT based keypoint matching. i-SIFT tackles the SIFT bottlenecks, e.g., extensive nearest neighbor indexing, by (i) significantly reducing the descriptor dimensionality, (ii) decreasing the size of object representation by one order of magnitude, and (iii) performing matching exclusively on attended descriptors, as required by resource sensitive devices. The key advantages of informative SIFT (i-SIFT) are demonstrated in a typical outdoor mobile vision experiment on the TSG-20 reference database, detecting buildings with high accuracy.
Original languageEnglish
Title of host publicationProceedings of the 1st Austrian Cognitive Vision Workshop 2005
EditorsM. Zillich, M. Vincze
PublisherAustrian Computer Society
Number of pages8
ISBN (Print)3-85403-186-6
Publication statusPublished - 2005
Externally publishedYes
Event1st Austrian Cognitive Vision Workshop, ACVW 2005 - Zell an der Pram, Austria
Duration: 31 Jan 200531 Jan 2005
Conference number: 1


Conference1st Austrian Cognitive Vision Workshop, ACVW 2005
Abbreviated titleACVW
CityZell an der Pram


  • n/a OA procedure


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