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Fusion-Based Classification: A Comparison of Early Fusion and Late Fusion Architecture for Content-Based Features
| Content Provider | Scilit |
|---|---|
| Author | Das, Rik |
| Copyright Year | 2020 |
| Description | Image data are a consortium of rich feature contents. A single technique for feature vector extraction is mostly not sufficient to describe the diverse content of a given image. This results in poor performance in terms of classification results. This limitation can be addressed by adopting a fusion-based approach. This chapter demonstrates two fusion-based approaches: early fusion and late fusion. The former is related to feature fusion, and the latter is described by classification decision fusion. Both techniques are compared to evaluate classification performances using diverse classification metrics. Book Name: Content-Based Image Classification |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2019-0-05480-8&isbn=9780429352928&doi=10.1201/9780429352928-8&format=pdf |
| Ending Page | 160 |
| Page Count | 14 |
| Starting Page | 147 |
| DOI | 10.1201/9780429352928-8 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-12-17 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Content-based Image Classification Cybernetical Science Diverse Classification Fusion Based Early Fusion and Late Fusion |
| Content Type | Text |
| Resource Type | Chapter |
