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Lp-norm IDF for Scalable Image Retrieval (2013)
| Citations: | 2 - 1 self |
BibTeX
@MISC{Zheng13lp-normidf,
author = {Liang Zheng and Shengjin Wang and Qi Tian},
title = {Lp-norm IDF for Scalable Image Retrieval},
year = {2013}
}
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Abstract
The Inverse Document Frequency (IDF) is prevalently utilized in the Bag-of-Words based image retrieval application. The basic idea is to assign less weight to terms with high frequency, and vice versa. However, in the conventional IDF routine, the estimation of visual word frequency is coarse and heuristic. Therefore, its effectiveness is largely compromised and far from optimal. To address this problem, this paper introduces a novel IDF family by the use of Lp-norm pooling technique. Carefully designed, the proposed IDF takes into account the term frequency, document frequency, the complexity of images, as well as the codebook information. We further propose a parameter tuning strategy which helps produce optimal balancing between TF and pIDF weights, yielding the so-called Lp-norm IDF (pIDF). We show that the conventional IDF is a special case of our generalized version, and two novel IDFs, i.e., the average IDF
Keyphrases
scalable image retrieval lp-norm idf conventional idf routine special case novel idf generalized version codebook information vice versa lp-norm pooling technique term frequency novel idf family image retrieval ap-plication so-called lp-norm idf visual word frequency document frequency conventional idf inverse document frequency high frequency average idf pidf weight basic idea
