Kolekar et al., 2004 - Google Patents
Hidden Markov Model Based Structuring of Cricket Video Sequences Using Motion and Color Features.Kolekar et al., 2004
View PDF- Document ID
- 842841440016251579
- Author
- Kolekar M
- Sengupta S
- Publication year
- Publication venue
- ICVGIP
External Links
Snippet
In this paper, we propose our techniques and results on automatic analysis of cricket video to facilitate highlight generation and content-based retrieval. We use Dynamic Programming based on Hidden Markov Model (HMM-DP) approach for structure analysis of cricket video …
- 241000238814 Orthoptera 0 title abstract description 40
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
- G06F17/30799—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content
- G06F17/30811—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content using motion, e.g. object motion, camera motion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
- G06F17/30799—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content
- G06F17/30802—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content using colour or luminescence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30837—Query results presentation or summarisation specifically adapted for the retrieval of video data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/00711—Recognising video content, e.g. extracting audiovisual features from movies, extracting representative key-frames, discriminating news vs. sport content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Giancola et al. | Temporally-aware feature pooling for action spotting in soccer broadcasts | |
| Zhu et al. | Player action recognition in broadcast tennis video with applications to semantic analysis of sports game | |
| CN102334118B (en) | Promoting method and system for personalized advertisement based on interested learning of user | |
| Gong et al. | Machine learning for multimedia content analysis | |
| US20050125223A1 (en) | Audio-visual highlights detection using coupled hidden markov models | |
| Fan et al. | Online learning of hierarchical Pitman–Yor process mixture of generalized Dirichlet distributions with feature selection | |
| JP2005196750A (en) | How to learn video structure | |
| Yan et al. | A new action recognition framework for video highlights summarization in sporting events | |
| Ferman et al. | Probabilistic analysis and extraction of video content | |
| Lien et al. | Scene-based event detection for baseball videos | |
| Hsu et al. | Generative, discriminative, and ensemble learning on multi-modal perceptual fusion toward news video story segmentation | |
| Raval et al. | A survey on event detection based video summarization for cricket | |
| Huang et al. | Joint video scene segmentation and classification based on hidden Markov model | |
| JP4271930B2 (en) | A method for analyzing continuous compressed video based on multiple states | |
| Kolekar et al. | Hidden Markov Model Based Structuring of Cricket Video Sequences Using Motion and Color Features. | |
| Abbasnejad et al. | Complex event detection using joint max margin and semantic features | |
| Gade et al. | Audio-visual classification of sports types | |
| Kolekar et al. | Hidden markov model based video indexing with discrete cosine transform as a likelihood function | |
| Lu et al. | An automatic video classification system based on a combination of HMM and video summarization | |
| Mochizuki et al. | Baseball video indexing using patternization of scenes and hidden Markov model | |
| Barnard et al. | Sports event recognition using layered HMMs | |
| Mei et al. | Structure and event mining in sports video with efficient mosaic | |
| Wang et al. | An ICA mixture hidden conditional random field model for video event classification | |
| Ando et al. | A robust scene recognition system for baseball broadcast using data-driven approach | |
| Sigari et al. | Sport video classification using an ensemble classifier |