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Updated: Apr 28, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

L(p) -norm IDF for scalable image retrieval.

Liang Zheng, Shengjin Wang, Qi Tian

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 12, 2014

Summary

This summary is machine-generated.

This study introduces a novel Lp-norm IDF (pIDF) for image retrieval, improving accuracy by considering image complexity and term frequency. The new method significantly boosts performance on large datasets without extra computational cost.

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Area of Science:

Computer Science
Information Retrieval
Machine Learning

Background:

Inverse Document Frequency (IDF) is crucial for bag-of-words image retrieval.
Conventional IDF methods use coarse estimations of visual word frequency, limiting effectiveness.
Existing approaches struggle with visual word burstiness and optimal term weighting.

Purpose of the Study:

To introduce a novel Lp-norm IDF (pIDF) family for enhanced image retrieval.
To address limitations in conventional IDF by incorporating image complexity and codebook information.
To improve the balancing of term frequency (TF) and inverse document frequency (IDF) weights.

Main Methods:

Developed a novel IDF family using Lp-norm pooling.
Proposed a parameter tuning strategy for optimal TF-pIDF weight balancing.
Incorporated term frequency, document frequency, image complexity, and codebook information into pIDF.

Main Results:

Achieved a +13.0% mean average precision improvement over TF-IDF on a 1M dataset.
Demonstrated effectiveness on large-scale databases and with codebooks trained on irrelevant data.
Showcased wide applicability across diverse image categories (buildings, objects, scenes).

Conclusions:

The proposed pIDF offers a generalized and improved approach to IDF for image retrieval.
pIDF effectively alleviates the visual word burstiness phenomenon.
The method is computationally efficient, with offline computation incurring no extra system cost.