MaskSearch: Querying Image Masks at Scale
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913187980378112 |
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| author | He, Dong Zhang, Jieyu Daum, Maureen Ratner, Alexander Balazinska, Magdalena |
| author_facet | He, Dong Zhang, Jieyu Daum, Maureen Ratner, Alexander Balazinska, Magdalena |
| contents | Machine learning tasks over image databases often generate masks that annotate image content (e.g., saliency maps, segmentation maps, depth maps) and enable a variety of applications (e.g., determine if a model is learning spurious correlations or if an image was maliciously modified to mislead a model). While queries that retrieve examples based on mask properties are valuable to practitioners, existing systems do not support them efficiently. In this paper, we formalize the problem and propose MaskSearch, a system that focuses on accelerating queries over databases of image masks while guaranteeing the correctness of query results. MaskSearch leverages a novel indexing technique and an efficient filter-verification query execution framework. Experiments with our prototype show that MaskSearch, using indexes approximately 5% of the compressed data size, accelerates individual queries by up to two orders of magnitude and consistently outperforms existing methods on various multi-query workloads that simulate dataset exploration and analysis processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_02375 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MaskSearch: Querying Image Masks at Scale He, Dong Zhang, Jieyu Daum, Maureen Ratner, Alexander Balazinska, Magdalena Databases Machine Learning Multimedia Machine learning tasks over image databases often generate masks that annotate image content (e.g., saliency maps, segmentation maps, depth maps) and enable a variety of applications (e.g., determine if a model is learning spurious correlations or if an image was maliciously modified to mislead a model). While queries that retrieve examples based on mask properties are valuable to practitioners, existing systems do not support them efficiently. In this paper, we formalize the problem and propose MaskSearch, a system that focuses on accelerating queries over databases of image masks while guaranteeing the correctness of query results. MaskSearch leverages a novel indexing technique and an efficient filter-verification query execution framework. Experiments with our prototype show that MaskSearch, using indexes approximately 5% of the compressed data size, accelerates individual queries by up to two orders of magnitude and consistently outperforms existing methods on various multi-query workloads that simulate dataset exploration and analysis processes. |
| title | MaskSearch: Querying Image Masks at Scale |
| topic | Databases Machine Learning Multimedia |
| url | https://arxiv.org/abs/2305.02375 |