Multimodal Retrieval by Text---Segment Biclustering
ADVANCES IN MULTILINGUAL AND MULTIMODAL INFORMATION RETRIEVAL(2008)
摘要
We describe our approach to the ImageCLEFphoto 2007 task. The novelty of our method consists of biclustering image segments
and annotation words. Given the query words, it is possible to select the image segment clusters that have strongest cooccurrence
with the corresponding word clusters. These image segment clusters act as the selected segments relevant to a query. We rank
text hits by our own tf.idf-based information retrieval system and image similarities by using a 20-dimensional vector describing
the visual content of an image segment. Relevant image segments were selected by the biclustering procedure. Images were segmented
by graph-based segmentation. We used neither query expansion nor relevance feedback; queries were generated automatically
from the title and the description words. The later were weighted by 0.1.
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关键词
segment biclustering,multimodal retrieval,image segment,20-dimensional vector,biclustering procedure,image segment cluster,query word,image similarity,selected segment,relevant image segment,query expansion,biclustering image segment,information retrieval system,image segmentation,text segmentation
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