| dc.contributor.author | Gibson Kimutai, Wilson Cheruiyot, Calvins Otieno | |
| dc.date.accessioned | 2022-01-23T11:59:39Z | |
| dc.date.available | 2022-01-23T11:59:39Z | |
| dc.date.issued | 2018 | |
| dc.identifier.issn | :2319-7242 | |
| dc.identifier.uri | https://repository.maseno.ac.ke/handle/123456789/4589 | |
| dc.description.abstract | In the last decade, large database of images have grown rapidly. This trend is expected to continue in to the
future. Retrieval and querying of these image in efficient way is a challenge in order to access the visual
content from large database. Content Based Image Retrieval (CBIR) provides the solution for efficient
retrieval of image from these huge image database. Many research efforts have been directed to this area
with color feature being the mostly used feature because of its ease of extraction. Although many research
efforts have been directed to this area, precision of majority of the developed models are still at less than
80%. This is a challenge as it leads to unsatisfying search results. This paper proposes a Content Based
Image Retrieval model for E-Commerce. | en_US |
| dc.publisher | International Journal of Engineering and Computer Science | en_US |
| dc.subject | Image retrieval, text-based image retrieval, content-based image retrieval, E-Commerce, E-Bay, shape, performance evaluation, precision, recall | en_US |
| dc.title | A Content Based Image Retrieval Model for E-Commerce | en_US |
| dc.type | Article | en_US |
