• VILLarD - Maintenance of large databases based on visual information using incremental learning
The Client : Javna agencija za raziskovalno dejavnost RS, Ljubljanski urbanistični zavod d.d. ( VILLarD )
Project type: Research projects ARRS
Project duration: 2014 - 2017
  • Description

We live in the era of information abundance. However, rather than quantity, the central concern is becoming the quality and credibility of the acquired data. This is especially true for visual information databases. Although the field of computer vision has achieved significant progress recently, the methods for automatic image interpretation are still not reliable enough to be used for autonomous annotation and maintenance of image and video databases (e.g. databases of detected objects). On the other hand, manual annotation of video sequences with relevant objects is very time consuming, expensive, as well as tedious and therefore prone to errors.

In this project we aspire to combining two approaches: computer-based automation of image interpretation that is necessary for database maintenance as well as suitable introduction of a human verifier into the loop. Such combination is of central importance for developing a methodology suitable for semiautomatic maintenance of traffic signalization records, which is partially our projects practical goal. Even the database of such records for only state roads in the Republic of Slovenia may contain more than 250.000 entries along with additional information. Automation is therefore crucial for continuous maintenance of such databases. The main goal of the project is to develop a framework for semi-supervised incremental learning as well as specific methods for visual learning and recognition that will increase the quality and efficiency of large visual information databases maintenance.

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