Phrasal recognition

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

In this paper, we introduce visual phrases, complex visual composites like 'a person riding a horse.' Visual phrases often display significantly reduced visual complexity compared to their component objects because the appearance of those objects can change profoundly when they participate in relations. We introduce a dataset suitable for phrasal recognition that uses familiar PASCAL object categories, and demonstrate significant experimental gains resulting from exploiting visual phrases. We show that a visual phrase detector significantly outperforms a baseline which detects component objects and reasons about relations, even though visual phrase training sets tend to be smaller than those for objects. We argue that any multiclass detection system must decode detector outputs to produce final results; this is usually done with nonmaximum suppression. We describe a novel decoding procedure that can account accurately for local context without solving difficult inference problems. We show this decoding procedure outperforms the state of the art. Finally, we show that decoding a combination of phrasal and object detectors produces real improvements in detector results.

Original languageEnglish
Article number6587714
Pages (from-to)2854-2865
Number of pages12
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume35
Issue number12
DOIs
Publication statusPublished - 2013
Externally publishedYes

Keywords

  • Visual phrase
  • object interactions
  • object recognition
  • object subcategories
  • phrasal recognition
  • scene understanding
  • single image activity recognition
  • visual composites

Fingerprint

Dive into the research topics of 'Phrasal recognition'. Together they form a unique fingerprint.

Cite this