Information Gain Study for Visual Vocabulary Construction
Authors: 
Huu Ton Le, Syntyche Gbèhounou, Thierry Urruty, François Lecellier, Christine FernandezAuthors Info & Claims
Huu Ton Le, Syntyche Gbèhounou, Thierry Urruty, François Lecellier, Christine FernandezAuthors Info & ClaimsPages 503 - 506
Abstract
Content Based Image Retrieval (CBIR) systems retrieve the most similar images to a query image in a collection. One of the most popular models and widely applied in this task is the Bag of Visual Words model (BoVW). In this paper, we introduce an evaluation study of different information gain models used for the construction of a visual word vocabulary. In the proposed framework, the information gain is used as discriminative information to index image features and select the ones that have the highest values of information gain. The empirical experiments made for this study evaluate the effect of four different information gain models: tf-idf, entropy, bm25, tfc with respect to different descriptors and image databases. The results show that selecting the image features based on at least one of the studied information gain model allows the retrieval process to be more accurate than the classical Bag of Visual Words model.
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Information & Contributors
Information
Published In
June 2015
700 pages
ISBN:9781450332743
DOI:10.1145/2671188
- General Chairs:
- Alex Hauptmann,
- Chong-Wah Ngo,
- Xiangyang Xue,
- Program Chairs:
- Yu-Gang Jiang,
- Cees Snoek,
- Nuno Vasconcelos
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Published: 22 June 2015
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