The acquisition of lexical semantics for spatial terms: a connectionist model of perceptual categorization
Computer vision techniques for PDA accessibility of in-house video surveillance
IWVS '03 First ACM SIGMM international workshop on Video surveillance
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Mining temporal patterns of movement for video content classification
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Video search in concept subspace: a text-like paradigm
Proceedings of the 6th ACM international conference on Image and video retrieval
State-of-the-art on spatio-temporal information-based video retrieval
Pattern Recognition
Interactive retrieval for multi-camera surveillance systems featuring spatio-temporal summarization
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Applying computational models of spatial prepositions to visually situated dialog
Computational Linguistics
Towards surveillance video search by natural language query
Proceedings of the ACM International Conference on Image and Video Retrieval
EELC'06 Proceedings of the Third international conference on Emergence and Evolution of Linguistic Communication: symbol Grounding and Beyond
Toward understanding natural language directions
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
A game-theoretic approach to generating spatial descriptions
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
| Hi-index | 0.00 |
Spatial language video retrieval is an important real-world problem that forms a test bed for evaluating semantic structures for natural language descriptions of motion on naturalistic data. Video search by natural language query requires that linguistic input be converted into structures that operate on video in order to find clips that match a query. This paper describes a framework for grounding the meaning of spatial prepositions in video. We present a library of features that can be used to automatically classify a video clip based on whether it matches a natural language query. To evaluate these features, we collected a corpus of natural language descriptions about the motion of people in video clips. We characterize the language used in the corpus, and use it to train and test models for the meanings of the spatial prepositions "to," "across," "through," "out," "along," "towards," and "around." The classifiers can be used to build a spatial language video retrieval system that finds clips matching queries such as "across the kitchen."