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Related Concept Videos

Ranks01:02

Ranks

Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
Active Filters01:25

Active Filters

Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
The Retina01:32

The Retina

The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.
Scanning Electron Microscopy01:07

Scanning Electron Microscopy

A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
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Related Experiment Videos

Active reranking for web image search.

Xinmei Tian1, Dacheng Tao, Xian-Sheng Hua

  • 1Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China. xinmei@mail.ustc.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 6, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces active reranking to improve image search by better understanding user intent. A novel method uses structural information and local-global dimension reduction to enhance search results with less user effort.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Information Retrieval
  • Machine Learning

Background:

  • Ambiguous query terms in image search lead to poor reranking performance.
  • Active reranking, incorporating user interactions, is crucial for improving search effectiveness.
  • A key challenge is accurately identifying and targeting user intent in active reranking.

Purpose of the Study:

  • To develop a novel active reranking scheme that effectively captures user intent.
  • To reduce user labeling efforts through an intelligent sample selection strategy.
  • To enhance the localization of user intent within the visual feature space.

Main Methods:

  • A structural information-based sample selection strategy is proposed to minimize user labeling.
  • A local-global discriminative dimension reduction algorithm is introduced for intent localization.
  • This algorithm learns a submanifold by transferring local geometry and discriminative information globally.

Main Results:

  • The proposed active reranking scheme demonstrates significant effectiveness.
  • The structural information-based sample selection reduces labeling burden.
  • The local-global discriminative dimension reduction algorithm accurately localizes user intent.

Conclusions:

  • The developed active reranking approach improves image search performance by addressing ambiguous queries.
  • The combination of structural information and advanced dimension reduction offers a robust solution for user intent capture.
  • Experimental validation on synthetic and real-world datasets confirms the efficacy of the proposed methods.