2012
On Combining Sequence Alignment and Feature-quantization for Sub-image Searching
HOMOLA, Tomáš, Vlastislav DOHNAL a Pavel ZEZULAZákladní údaje
Originální název
On Combining Sequence Alignment and Feature-quantization for Sub-image Searching
Autoři
HOMOLA, Tomáš (203 Česká republika, domácí), Vlastislav DOHNAL (203 Česká republika, garant, domácí) a Pavel ZEZULA (203 Česká republika, domácí)
Vydání
International Journal of Multimedia Data Engineering and Management (IJMDEM), Hershey PA 17033-1240, USA, IGI Global, 2012, 1947-8534
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Kód RIV
RIV/00216224:14330/12:00073206
Organizační jednotka
Fakulta informatiky
UT WoS
000218974600002
Klíčová slova anglicky
image matching; sub-image retrieval; local image features; sequence alignment; performance evaluation
Štítky
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 1. 4. 2015 09:01, RNDr. Pavel Šmerk, Ph.D.
Anotace
V originále
The availability of various photo archives and photo sharing systems made similarity searching much more important because the photos are not usually conveniently tagged. So the photos (images) need to be searched by their content. Moreover, it is important not only to compare images with a query holistically but also to locate images that contain the query as their part. The query can be a picture of a person, building, or an abstract object and the task is to retrieve images of the query object but from a different perspective or images capturing a global scene containing the query object. This retrieval is called the sub-image searching. In this paper, we propose an algorithm, called SASISA, for retrieving database images by their similarity to and containment of a query. The novelty of it lies in application of a sequence alignment algorithm, which is commonly used in text retrieval. This forms an orthogonal solution to currently used approaches based on inverted files. We improve efficiency of SASISA by applying vector-quantization of local image feature descriptors. The proposed algorithm and its optimization are evaluated on a real-life data set containing photographs where images of logos are searched. It is compared to a state-of-the-art method (Joly & Buisson, 2009) and the improvement of 16% in mean average precision (mAP) is obtained.
Návaznosti
GBP103/12/G084, projekt VaV |
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VF20102014004, projekt VaV |
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