Video shot retrieval using a kernel derived from a continuous HMM

Atulya Velivelli, Thomas S. Huang, Alexander Hauptmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we propose a discriminative approach for retrieval of video shots characterized by a sequential structure. The task of retrieving shots similar in content to a few positive example shots is more close to a binary classification problem. Hence, this task can be solved by a discriminative learning approach. For a content-based retrieval task the twin characteristics of rare positive example occurrence and a sequential structure in the positive examples make it attractive for us to use a learning approach based on a generative model like HMM. To make use of the positive aspects of both discriminative and generative models, we derive Fisher and Modified score kernels for a Continuous HMM and incorporate them into SVM classification framework. The training set video shots are used to learn SVM classifier. A test set video shot is ranked based on its proximity to the positive class side of hyperplane. We evaluate the performance of the derived kernels by retrieving video shots of airplane takeoff. The retrieval performance using the derived kernels is found to be much better compared to linear and RBF kernels.

Original languageEnglish (US)
Title of host publicationMultimedia Content Analysis, Management, and Retrieval 2006 - Proceedings of SPIE-IS and T Electronic Imaging
ISBN (Print)081946113X, 9780819461131
StatePublished - 2006
EventMultimedia Content Analysis, Management, and Retrieval 2006 - San Jose, CA, United States
Duration: Jan 17 2006Jan 19 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherMultimedia Content Analysis, Management, and Retrieval 2006
Country/TerritoryUnited States
CitySan Jose, CA


  • Continuous HMM
  • Fisher kernel
  • Modified score kernel
  • Retrieval
  • SVM

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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