LVQ-based video object segmentation through combination of spatial and color features

Hariadi Mochamad, Hui Chien Loy, Takafumi Aoki

Research output: Contribution to conferencePaperpeer-review

Abstract

This paper proposes semi-automatic video object segmentation using Learning Vector Quantization (LVQ). For each video frame, we use 5-D feature vectors whose components are spatial information in pixel coordinates and color information in YUV color space. First, the object of interest and its background are defined with human assistance. Both the object of interest and its background are then used to train LVQ codebook vectors to approximate the object shape. Next, the LVQ codebook vectors are used to segment the object of interest automatically for subsequent frames. We introduce a variable weight K for scaling 5-D vector to adjust the balance between spatial and color information for accurate segmentation. Experimental results show that the proposed algorithm is useful for tracking an object moving at moderate speed.

Original languageEnglish
Publication statusPublished - 2004 Dec 1
EventIEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering - Chiang Mai, Thailand
Duration: 2004 Nov 212004 Nov 24

Other

OtherIEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering
CountryThailand
CityChiang Mai
Period04/11/2104/11/24

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'LVQ-based video object segmentation through combination of spatial and color features'. Together they form a unique fingerprint.

Cite this