Mengye Song, Shengrong Gong, Chunping Liu, Yi Ji, Husheng Dong
{"title":"用颜色名称改进局部最大出现率的人物再识别","authors":"Mengye Song, Shengrong Gong, Chunping Liu, Yi Ji, Husheng Dong","doi":"10.1109/CISP.2015.7407963","DOIUrl":null,"url":null,"abstract":"Person re-identification is the task of associating people across cameras with non-overlapping view field. Two key aspects of Person re-identification are the feature representation and metric learning. The feature representation employed should be both discriminative and invariant, which is also our considering in this paper. To enhance person re-identification performance, we propose to combine improved Local Maximal Occurrence (LOMO) descriptor with semantic color names (SCN). Especially, we introduce symmetry information of human body to suppress the impact of background in LOMO. When fused with mid-level attribute-based description - sematic color names, our more discriminative signature is obtained. Based on the KISS metric, evaluation on the challenging VIPeR dataset shows that the proposed method improves the re-identification significantly.","PeriodicalId":167631,"journal":{"name":"2015 8th International Congress on Image and Signal Processing (CISP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Person re-identification by improved Local Maximal Occurrence with color names\",\"authors\":\"Mengye Song, Shengrong Gong, Chunping Liu, Yi Ji, Husheng Dong\",\"doi\":\"10.1109/CISP.2015.7407963\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Person re-identification is the task of associating people across cameras with non-overlapping view field. Two key aspects of Person re-identification are the feature representation and metric learning. The feature representation employed should be both discriminative and invariant, which is also our considering in this paper. To enhance person re-identification performance, we propose to combine improved Local Maximal Occurrence (LOMO) descriptor with semantic color names (SCN). Especially, we introduce symmetry information of human body to suppress the impact of background in LOMO. When fused with mid-level attribute-based description - sematic color names, our more discriminative signature is obtained. Based on the KISS metric, evaluation on the challenging VIPeR dataset shows that the proposed method improves the re-identification significantly.\",\"PeriodicalId\":167631,\"journal\":{\"name\":\"2015 8th International Congress on Image and Signal Processing (CISP)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 8th International Congress on Image and Signal Processing (CISP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CISP.2015.7407963\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 8th International Congress on Image and Signal Processing (CISP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISP.2015.7407963","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Person re-identification by improved Local Maximal Occurrence with color names
Person re-identification is the task of associating people across cameras with non-overlapping view field. Two key aspects of Person re-identification are the feature representation and metric learning. The feature representation employed should be both discriminative and invariant, which is also our considering in this paper. To enhance person re-identification performance, we propose to combine improved Local Maximal Occurrence (LOMO) descriptor with semantic color names (SCN). Especially, we introduce symmetry information of human body to suppress the impact of background in LOMO. When fused with mid-level attribute-based description - sematic color names, our more discriminative signature is obtained. Based on the KISS metric, evaluation on the challenging VIPeR dataset shows that the proposed method improves the re-identification significantly.