Recognizing image "style" and activities in video using local features and naive Bayes

Daniel Keren

doi:10.1016/S0167-8655(03)00152-1

Recognizing image "style" and activities in video using local features and naive Bayes

Daniel Keren

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

Abstract

The goal of this paper is to offer a framework for classification of images and video according to their "type", or "style" - a problem which is hard to define, but easy to illustrate; for example, identifying an artist by the style of his/her painting, or determining the activity in a video sequence. The paper offers a simple classification paradigm based on local properties of spatial or spatio-temporal blocks. The learning and classification are based on the naive Bayes classifier. A few experimental results are presented.

Original language	English
Pages (from-to)	2913-2922
Number of pages	10
Journal	Pattern Recognition Letters
Volume	24
Issue number	16
DOIs	https://doi.org/10.1016/S0167-8655(03)00152-1
State	Published - Dec 2003

Bibliographical note

Funding Information:
This research was supported by The Israel Science Foundation (grant no. 591/00-10.5).

Keywords

Activity detection
Image style
Naive Bayes
Texture

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/S0167-8655(03)00152-1

Cite this

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ER -

Recognizing image "style" and activities in video using local features and naive Bayes

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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