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- W2635858081 abstract "The availability of various photo archives and photo sharingsystems made similarity searching much more important becausethe photos are not usually conveniently tagged. So the photos(images) need to be searched by their content. Moreover, it isimportant not only to compare images with a query holisticallybut also to locate images that contain the query as their part.The query can be a picture of a person, building, or anabstract object and the task is to retrieve images of the queryobject but from a different perspective or images capturing aglobal scene containing the query object. This retrieval iscalled the sub-image searching. In this paper, we propose analgorithm, called SASISA, for retrieving database images bytheir similarity to and containment of a query. The novelty ofit lies in application of a sequence alignment algorithm, whichis commonly used in text retrieval. This forms an orthogonalsolution to currently used approaches based on inverted files.We improve efficiency of SASISA by applying vector-quantizationof local image feature descriptors. The proposed algorithm andits optimization are evaluated on a real-life data setcontaining photographs where images of logos are searched. Itis compared to a state-of-the-art method (Joly & Buisson, 2009)and the improvement of 16% in mean average precision (mAP) isobtained." @default.
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- W2635858081 date "2012-01-01" @default.
- W2635858081 modified "2023-09-27" @default.
- W2635858081 title "On Combining Sequence Alignment and Feature-quantization forSub-image Searching" @default.
- W2635858081 hasPublicationYear "2012" @default.
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