Exploiting Word and Visual Word Co-occurrence for Sketch-based Image Retrieval
Date Issued
2015
Date
2015
Author(s)
Liu, Ching-Hsuan
Abstract
As the increasing popularity of touch-screen devices, retrieving images by hand-drawn sketch has become a trend. Human sketch can easily express some complex user intention such as the object shape. However, sketches are sometimes ambiguous due to different drawing styles and inter-class object shape ambiguity. Although adding text queries as semantic information can help removing the ambiguity of sketch, it requires a huge amount of efforts to annotate text tags to all database images. We propose a method directly model the relationship between text and images by the co-occurrence relationship between words and visual words, which improves traditional sketch-based image retrieval (SBIR), provides a baseline performance and obtains more relevant results in the condition that all images in database do not have any text tag. Experimental results show that our method really can help SBIR to get better retrieval result since it indeed learned semantic meaning from the ``word-visual word"" (W-VW) co-occurrence relationship.
Subjects
sketch-based image retrieval
co-occurrence model
Type
thesis
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