Data-Driven Objectness
We propose a data-driven approach to estimate the likelihood that an image segment corresponds to a scene object (its "objectness") by comparing it to a large collection of example object regions. We demonstrate that when the application domain is known, for example, in our case activity o...
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 37(2015), 1 vom: 01. Jan., Seite 189-95 |
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| Auteur principal: | |
| Autres auteurs: | , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2015
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
| Sujets: | Journal Article Research Support, U.S. Gov't, Non-P.H.S. |
| Résumé: | We propose a data-driven approach to estimate the likelihood that an image segment corresponds to a scene object (its "objectness") by comparing it to a large collection of example object regions. We demonstrate that when the application domain is known, for example, in our case activity of daily living (ADL), we can capture the regularity of the domain specific objects using millions of exemplar object regions. Our approach to estimating the objectness of an image region proceeds in two steps: 1) finding the exemplar regions that are the most similar to the input image segment; 2) calculating the objectness of the image segment by combining segment properties, mutual consistency across the nearest exemplar regions, and the prior probability of each exemplar region. In previous work, parametric objectness models were built from a small number of manually annotated objects regions, instead, our data-driven approach uses 5 million object regions along with their metadata information. Results on multiple data sets demonstrates our data-driven approach compared to the existing model based techniques. We also show the application of our approach in improving the performance of object discovery algorithms |
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| Description: | Date Completed 24.11.2015 Date Revised 10.09.2015 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 1939-3539 |
| DOI: | 10.1109/TPAMI.2014.2315811 |