Notice: Undefined index: linkPowrot in C:\wwwroot\wwwroot\publikacje\publikacje.php on line 1275
Publikacje
Pomoc (F2)
[84940] Artykuł:

World Wide Web CBIR Searching Using Query by Approximate Shapes

(Wyszukianie obrazem w stronach WWW z użyciem zapytania poprzez przybliżone kształty)
Czasopismo: Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference, Advances in Intelligent Systems and Computing   Tom: 801, Strony: 85-93
ISSN:  2194-5365
ISBN:  978-3-319-99608-0
Wydawca:  SPRINGER INTERNATIONAL PUBLISHING AG, GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
Opublikowano: Styczeń 2019
Seria wydawnicza:  Advances in Intelligent Systems and Computing
 
  Autorzy / Redaktorzy / Twórcy
Imię i nazwisko Wydział Katedra Do oświadczenia
nr 3
Grupa
przynależności
Dyscyplina
naukowa
Procent
udziału
Liczba
punktów
do oceny pracownika
Liczba
punktów wg
kryteriów ewaluacji
Tomasz Michno orcid logo WEAiIKatedra Systemów Informatycznych *Niespoza "N" jednostkiAutomatyka, elektronika, elektrotechnika i technologie kosmiczne50.00.00  
Roman Stanisław Deniziak orcid logo WEAiIKatedra Systemów Informatycznych *Takzaliczony do "N"Automatyka, elektronika, elektrotechnika i technologie kosmiczne50.00.00  

Grupa MNiSW:  Materiały z konferencji międzynarodowej (zarejestrowane w Web of Science)
Punkty MNiSW: 0
Klasyfikacja Web of Science: Proceedings Paper


Pełny tekstPełny tekst     DOI LogoDOI     Web of Science Logo Web of Science    
Keywords:

CBIR Multimedia databases Query by sketch 



Abstract:

Nowadays more and more images are stored in the World Wide Web. There are a lot of photo galleries, media portals and social media portals where users add their own content, but also they would like to find the proper ones. The problem of searching for an image is not trivial. Objects present on images may have e.g. different colors, backgrounds or orientations. Moreover, the image may contain many other details which may be hard to be described by words. This paper presents a new system which may be used to query for images from the internet which is based on our Query by Approximate Shapes algorithm. The main idea of the proposed approach is to gather images from the internet. Next, all images are processed using our algorithm which is based on decomposing objects into a set of simple shapes. During the query, depending on its type, an example image or a sketch is used. For both types a graph is constructed which is compared with graphs in the database.



B   I   B   L   I   O   G   R   A   F   I   A
1. Deniziak, R.S., Michno, T.: Content based image retrieval using query by approximate shape. In: 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 807–816. IEEE, Gdańsk (2016). https://doi.org/10.15439/2016f233
2. Deniziak, R.S., Michno, T.: New content based image retrieval database structure using query by approximate shapes. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 613–621. IEEE, Prague (2017). https://doi.org/10.15439/2017F457
3. Deniziak, R.S., Michno, T.: Query by shape for image retrieval from multimedia databases. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) Beyond Databases, Architectures and Structures. CCIS, vol. 521, pp. 377–386. Springer, Ustroń (2015). https://doi.org/10.1007/978-3-319-18422-7_33
Google Scholar
4. Deniziak, R.S., Michno, T.: Query-by-shape interface for content based image retrieval. In: 2015 8th International Conference on Human System Interaction (HSI), pp. 108–114. IEEE, Warsaw, June 2015. https://doi.org/10.1109/HSI.2015.7170652
5. Deniziak, R.S., Michno, T., Krechowicz, A.: The scalable distributed two-layer content based image retrieval data store. In: 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 827–832. IEEE, Łódź (2015). https://doi.org/10.15439/2015F272
6. Kato, T., Kurita, T., Otsu, N., Hirata, K.: A sketch retrieval method for full color image database-query by visual example. In: [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition, pp. 530–533. IEEE, The Hague (1992). https://doi.org/10.1109/ICPR.1992.201616
7. Kriegel, H.P., Kroger, P., Kunath, P., Pryakhin, A.: Effective similarity search in multimedia databases using multiple representations. In: 2006 12th International Multi-Media Modelling Conference. IEEE, Beijing (2006). https://doi.org/10.1109/MMMC.2006.1651355
8. Lalos, C., Doulamis, A., Konstanteli, K., Dellias, P., Varvarigou, T.: An innovative content-based indexing technique with linear response suitable for pervasive environments. In: 2008 International Workshop on Content-Based Multimedia Indexing, pp. 462–469. IEEE, London (2008). https://doi.org/10.1109/CBMI.2008.4564983
9. Li, C.-Y., Hsu, C.-T.: Image retrieval with relevance feedback based on graph-theoretic region correspondence estimation. IEEE Trans. Multimedia 10(3), 447–456 (2008). https://doi.org/10.1109/tmm.2008.917421
CrossRefGoogle Scholar
10. Li, B., Lu, Y., Shen, J.: A semantic tree-based approach for sketch-based 3d model retrieval. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3880–3885. IEEE, Cancun (2016). https://doi.org/10.1109/ICPR.2016.7900240
11. Mocofan, M., Ermalai, I., Bucos, M., Onita, M., Dragulescu, B.: Supervised tree content based search algorithm for multimedia image databases. In: 2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), pp. 469–472. IEEE, Timisoara (2011). https://doi.org/10.1109/SACI.2011.5873049
12. Shih, T.K.: Distributed Multimedia Databases. IGI Global, Hershey (2002)
CrossRefGoogle Scholar
13. Sitek, P., Wikarek, J.: A hybrid programming framework for modeling and solving constraint satisfaction and optimization problems. Sci. Programm. 2016, Article ID 5102616 (2016). https://doi.org/10.1155/2016/5102616
CrossRefGoogle Scholar
14. Śluzek, A.: Machine vision in food recognition: attempts to enhance CBVIR tools. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Position Papers of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016. PTI, Gdańsk (2016). https://doi.org/10.15439/2016f579
15. Wang, H.H., Mohamad, D., Ismail, N.A.: Approaches, challenges and future direction of image retrieval. J. Comput. 2(6) (2010)
Google Scholar