<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The progress of image search engines still proceeds, but there are some challenges yet in complex queries. In this paper, we present a new semantic image search system, which is capable of multiple object retrieval using only visual content of the images. We have used the state-of-the-art image processing methods prior to the search, such as Fisher-vector and C-SVC classifier, in order to semantically classify images containing multiple objects. The results of this offline classification are stored for the latter search task. We have elaborated more search methods for combining the results of binary classifiers of objects in images. Our search methods use confidence values of object classifiers and after the evaluation, the best method is selected for thorough analysis. Our solution is compared with the famous web images search engines (Google, Bing and Flickr), and there is a comparison of their Mean Average Precision (MAP) values. It can be concluded that our system reaches the benchmark; moreover, in most cases our method outperforms the others, especially in the cases of queries with many objects.</p></abstract>

The progress of image search engines still proceeds, but there are some challenges yet in complex queries. In this paper, we present a new semantic image search system, which is capable of multiple object retrieval using only visual content of the images. We have used the state-of-the-art image processing methods prior to the search, such as Fisher-vector and C-SVC classifier, in order to semantically classify images containing multiple objects. The results of this offline classification are stored for the latter search task. We have elaborated more search methods for combining the results of binary classifiers of objects in images. Our search methods use confidence values of object classifiers and after the evaluation, the best method is selected for thorough analysis. Our solution is compared with the famous web images search engines (Google, Bing and Flickr), and there is a comparison of their Mean Average Precision (MAP) values. It can be concluded that our system reaches the benchmark; moreover, in most cases our method outperforms the others, especially in the cases of queries with many objects.

Content-Based Image Retrieval for Multiple Objects Search

Budapest University of Technology and Economics

Department of Telecommunications and Media Informatics

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Content-Based Image Retrieval for Multiple Objects Search"}

The progress of image search engines still proceeds, but there are some challenges yet in complex queries. In this paper, we present a new semantic image...

Content-Based Image Retrieval for Multiple Objects Search

Data publikacji: 26 cze 2017

Zakres stron: 106 - 118

DOI: https://doi.org/10.1515/cait-2017-0020

Słowa kluczowe
Visual content, image search, classification, search engine, multiple objects

© 2017 Gábor Szűcs et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Content-Based Image Retrieval for Multiple Objects Search

Data publikacji: 26 cze 2017

Zakres stron: 106 - 118

DOI: https://doi.org/10.1515/cait-2017-0020

Słowa kluczoweVisual content, image search, classification, search engine, multiple objects

© 2017 Gábor Szűcs et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Visual content, image search, classification, search engine, multiple objects