Histograms of local appearance descriptors are a popular representation for visual recognition. They are highly discriminant and have good resistance to local occlusions and to geometric and photometric variations, but they are not able to exploit spatial co-occurrence statistics at scales larger than their local input patches. We present a new multilevel visual representation, \u2018hyperfeatures\u2019, that is designed to remedy this. The starting point is the familiar notion that to detect object parts, in practice it often suf\ufb01ces to detect co-occurrences of more local object fragments \u2013 a process that can be formalized as comparison (e.g. vector quantization) of image patches against a codebook of known fragments, followed by local aggregation of the resulting codebook membership vectors to detect cooccurrences. This process converts local collections of image descriptor vectors into somewhat less local histogram vectors \u2013 higher-level but spatially coarser descriptors. We observe that as the output is again a local descriptor vector, the process can be iterated, and that doing so captures and codes ever larger assemblies of object parts and increasingly abstract or \u2018semantic\u2019 image properties. We formulate the hyperfeatures model and study its performance under several different image coding methods including clustering based Vector Quantization, Gaussian Mixtures, and combinations of these with Latent Dirichlet Allocation. We \ufb01nd that the resulting high-level features provide improved performance in several object image and texture image classi\ufb01cation tasks.<\/p>\n","protected":false},"excerpt":{"rendered":"
Histograms of local appearance descriptors are a popular representation for visual recognition. They are highly discriminant and have good resistance to local occlusions and to geometric and photometric variations, but they are not able to exploit spatial co-occurrence statistics at scales larger than their local input patches. We present a new multilevel visual representation, \u2018hyperfeatures\u2019, […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the European Conference on Computer Vision","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"30-43","msr_page_range_start":"30","msr_page_range_end":"43","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of the European Conference on Computer Vision","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"A. Agarwal, B. 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