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Related Concept Videos

Visual System01:26

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
Vision01:24

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
Visual Agnosia01:12

Visual Agnosia

Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round end"...
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Design Example: Aggregate Gradation01:24

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Related Experiment Video

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Generating descriptive visual words and visual phrases for large-scale image applications.

Shiliang Zhang1, Qi Tian, Gang Hua

  • 1Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. slzhang@jdl.ac.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 23, 2011
PubMed
Summary

This study introduces descriptive visual words (DVWs) and phrases (DVPs) to improve image representation beyond traditional Bag-of-visual Words (BoWs). These new elements enhance image retrieval and recognition tasks, outperforming existing methods in accuracy and efficiency.

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Visualizing Visual Adaptation
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Last Updated: Jun 3, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Area of Science:

  • Computer Vision
  • Multimedia Analysis
  • Image Representation

Background:

  • Traditional Bag-of-visual Words (BoWs) models face limitations in effectiveness due to visual vocabulary derived from single-image local descriptors.
  • Images contain rich visual information about objects and scenes, necessitating improved methods for their representation.

Purpose of the Study:

  • To propose descriptive visual words (DVWs) and descriptive visual phrases (DVPs) as enhanced visual correspondences to text words and phrases.
  • To develop a general framework for generating DVWs and DVPs for diverse image applications.
  • To evaluate the effectiveness of DVWs and DVPs in improving image retrieval, search re-ranking, and object recognition.

Main Methods:

  • A general framework was developed to identify and generate DVWs and DVPs from a large-scale image database (1506 object/scene categories).
  • DVWs represent individual descriptive visual elements, while DVPs capture frequently co-occurring visual word pairs.
  • The proposed methods were applied to large-scale near-duplicated image retrieval, image search re-ranking, and object recognition tasks.

Main Results:

  • DVWs and DVPs were found to be more informative and descriptive than classic visual words, offering better comparability to text words.
  • The combination of DVW and DVP significantly improved large-scale near-duplicated image retrieval performance (accuracy, efficiency, memory consumption).
  • The DWPRank algorithm for image search re-ranking achieved a 12.4% increase in mean average precision and was 11 times faster than state-of-the-art methods.

Conclusions:

  • Descriptive visual words and phrases offer a more robust and effective approach to image representation compared to traditional methods.
  • The proposed framework and elements demonstrate significant advancements in key computer vision applications, particularly in large-scale image retrieval and search.
  • DVWs and DVPs pave the way for more accurate, efficient, and memory-conscious image analysis systems.