Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Fixed Action Patterns

Fixed Action Patterns

A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

<i>LsToll</i> Gene Mediates Antibacterial Immunity and Developmental Regulation in <i>Loxostege sticticalis</i>.

Insects·2026

Same author

Long-Term Outcomes From a Decade of Donation After Circulatory Death Heart Transplantation in Australia.

JACC. Heart failure·2026

Same author

Rapid Determination of Several Biogenic Amines in Cold-Chain Fish Samples by Portable Ion Trap Mass Spectrometry with Nano-Electrospray Ionization.

Foods (Basel, Switzerland)·2026

Same author

Aerosol Inhalation of a Recombinant H7N9 Hemagglutinin Antigen Elicits Systemic and Mucosal Immune Responses in Mice.

Viruses·2026

Same author

Localized hybridization chain reaction on self-assembled DNA nanospheres overcomes the kinetics-stability-complexity trilemma in miRNA sensing.

Biosensors & bioelectronics·2026

Same author

Gastrointestinal dysfunction in patients with Mowat-Wilson syndrome is associated with feeding difficulties and altered plasma neurotransmitters.

Frontiers in pediatrics·2026

Search research articles

Related Experiment Video

Updated: Mar 30, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

Good Practices for Learning to Recognize Actions Using FV and VLAD.

Jianxin Wu, Yu Zhang, Weiyao Lin

IEEE Transactions on Cybernetics

|November 10, 2015

Summary

This study enhances action recognition in videos using Fisher vectors (FV) and vectors of locally aggregated descriptors (VLAD). New methods improve accuracy and significantly reduce storage needs for large-scale video data analysis.

More Related Videos

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

Published on: October 12, 2011

Quantifying Fish Swimming Behavior in Response to Acute Exposure of Aqueous Copper Using Computer Assisted Video and Digital Image Analysis

Quantifying Fish Swimming Behavior in Response to Acute Exposure of Aqueous Copper Using Computer Assisted Video and Digital Image Analysis

Published on: February 26, 2016

Related Experiment Videos

Last Updated: Mar 30, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

Published on: October 12, 2011

Quantifying Fish Swimming Behavior in Response to Acute Exposure of Aqueous Copper Using Computer Assisted Video and Digital Image Analysis

Quantifying Fish Swimming Behavior in Response to Acute Exposure of Aqueous Copper Using Computer Assisted Video and Digital Image Analysis

Published on: February 26, 2016

Area of Science:

Computer Vision
Machine Learning
Video Analysis

Background:

High-dimensional representations like Fisher vectors (FV) and vectors of locally aggregated descriptors (VLAD) achieve high accuracy in video action recognition.
The high dimensionality of these features poses computational challenges for large-scale video datasets.

Purpose of the Study:

To investigate and improve the effectiveness of high-dimensional representations for action recognition in videos.
To address the computational and storage challenges associated with FV and VLAD for large-scale video analysis.

Main Methods:

Reviewed and empirically evaluated existing techniques for FV and VLAD in image classification for their applicability to action recognition.
Proposed a novel pooling strategy for VLAD and three efficient transformations for both FV and VLAD.
Developed new feature selection and compression methods for FV and VLAD representations.

Main Results:

Normality and bimodality were identified as crucial factors for achieving high accuracy in action recognition.
The proposed pooling strategy and transformations demonstrated superior accuracy compared to original FV/VLAD methods.
Feature selection and compression methods reduced storage by 96% while maintaining or improving accuracy.

Conclusions:

The study provides a set of recommended best practices for action recognition using FV and VLAD representations.
The proposed methods offer a practical solution for efficient and accurate large-scale video action recognition.
Optimized FV and VLAD representations are essential for scalable and effective video analysis.