{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/compositional-model-based-fisher-vector","title":"Compositional Model based Fisher Vector Coding for Image Classification","arxiv_id":"1601.04143","date":"2016-01-16","proceeding":null,"authors":["Lingqiao Liu","Peng Wang","Chunhua Shen","Lei Wang","Anton Van Den Hengel","Chao Wang","Heng Tao Shen"],"abstract":"Deriving from the gradient vector of a generative model of local features,\nFisher vector coding (FVC) has been identified as an effective coding method\nfor image classification. Most, if not all, FVC implementations employ the\nGaussian mixture model (GMM) to depict the generation process of local\nfeatures. However, the representative power of the GMM could be limited because\nit essentially assumes that local features can be characterized by a fixed\nnumber of feature prototypes and the number of prototypes is usually small in\nFVC. To handle this limitation, in this paper we break the convention which\nassumes that a local feature is drawn from one of few Gaussian distributions.\nInstead, we adopt a compositional mechanism which assumes that a local feature\nis drawn from a Gaussian distribution whose mean vector is composed as the\nlinear combination of multiple key components and the combination weight is a\nlatent random variable. In this way, we can greatly enhance the representative\npower of the generative model of FVC. To implement our idea, we designed two\nparticular generative models with such a compositional mechanism.","url_abs":"http://arxiv.org/abs/1601.04143v3","url_pdf":"http://arxiv.org/pdf/1601.04143v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"compositional-model-based-fisher-vector","repo_url":"https://bitbucket.org/chhshen/fishercoding","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}