Bag-of-visual-words vs global image descriptors on two-stage multimodal retrieval

The Bag-Of-Visual-Words (BOVW) paradigm is fast becoming a popular image representation for Content-Based Image Retrieval (CBIR), mainly because of its better retrieval effectiveness over global feature representations on collections with images being nearduplicate to queries. In this experimental study we demonstrate that this advantage of BOVW is diminished when visual diversity is enhanced by using a secondary modality, such as text, to pre-filter images. The TOP-SURF descriptor is evaluated against Compact Composite Descriptors on a two-stage image retrieval setup, which first uses a text modality to rank the collection and then perform CBIR only on the top-K items. Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—retrieval models, search process General Terms: Measurement, Experimentation, Theory

• Type: Journal
• Year: 2011
• Where: SIGIR
• Authors: Savvas A. Chatzichristofis, Konstantinos Zagoris, Avi Arampatzis
• Keywords: Based Image Retrieval, Feature Representations, Information Storage And Retrieval, Information Technology, SIGIR 2011