Harvesting image databases from the Web
The objective of this work is to automatically generate a large number of images for a specified object class. A multimodal approach employing both text, metadata, and visual features is used to gather many high-quality images from the Web. Candidate images are obtained by a text-based Web search qu...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
IEEE
2010
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| _version_ | 1824459048078016512 |
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| author | Schroff, F Criminisi, A Zisserman, A |
| author_facet | Schroff, F Criminisi, A Zisserman, A |
| author_sort | Schroff, F |
| collection | OXFORD |
| description | The objective of this work is to automatically generate a large number of images for a specified object class. A multimodal approach employing both text, metadata, and visual features is used to gather many high-quality images from the Web. Candidate images are obtained by a text-based Web search querying on the object identifier (e.g., the word penguin). The Webpages and the images they contain are downloaded. The task is then to remove irrelevant images and rerank the remainder. First, the images are reranked based on the text surrounding the image and metadata features. A number of methods are compared for this reranking. Second, the top-ranked images are used as (noisy) training data and an SVM visual classifier is learned to improve the ranking further. We investigate the sensitivity of the cross-validation procedure to this noisy training data. The principal novelty of the overall method is in combining text/metadata and visual features in order to achieve a completely automatic ranking of the images. Examples are given for a selection of animals, vehicles, and other classes, totaling 18 classes. The results are assessed by precision/recall curves on ground-truth annotated data and by comparison to previous approaches, including those of Berg and Forsyth and Fergus et al. |
| first_indexed | 2025-02-19T04:35:35Z |
| format | Journal article |
| id | oxford-uuid:ed2c13e4-679d-492c-8eda-d3a1282be2c0 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2025-02-19T04:35:35Z |
| publishDate | 2010 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:ed2c13e4-679d-492c-8eda-d3a1282be2c02025-01-23T14:33:26ZHarvesting image databases from the WebJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ed2c13e4-679d-492c-8eda-d3a1282be2c0EnglishSymplectic ElementsIEEE2010Schroff, FCriminisi, AZisserman, AThe objective of this work is to automatically generate a large number of images for a specified object class. A multimodal approach employing both text, metadata, and visual features is used to gather many high-quality images from the Web. Candidate images are obtained by a text-based Web search querying on the object identifier (e.g., the word penguin). The Webpages and the images they contain are downloaded. The task is then to remove irrelevant images and rerank the remainder. First, the images are reranked based on the text surrounding the image and metadata features. A number of methods are compared for this reranking. Second, the top-ranked images are used as (noisy) training data and an SVM visual classifier is learned to improve the ranking further. We investigate the sensitivity of the cross-validation procedure to this noisy training data. The principal novelty of the overall method is in combining text/metadata and visual features in order to achieve a completely automatic ranking of the images. Examples are given for a selection of animals, vehicles, and other classes, totaling 18 classes. The results are assessed by precision/recall curves on ground-truth annotated data and by comparison to previous approaches, including those of Berg and Forsyth and Fergus et al. |
| spellingShingle | Schroff, F Criminisi, A Zisserman, A Harvesting image databases from the Web |
| title | Harvesting image databases from the Web |
| title_full | Harvesting image databases from the Web |
| title_fullStr | Harvesting image databases from the Web |
| title_full_unstemmed | Harvesting image databases from the Web |
| title_short | Harvesting image databases from the Web |
| title_sort | harvesting image databases from the web |
| work_keys_str_mv | AT schrofff harvestingimagedatabasesfromtheweb AT criminisia harvestingimagedatabasesfromtheweb AT zissermana harvestingimagedatabasesfromtheweb |