A k-Nearest Neighbor Multilabel Ranking Algorithm with Application to Content-based Image Retrieval - 2017

PROJECT TITLE :

A k-Nearest Neighbor Multilabel Ranking Algorithm with Application to Content-based Image Retrieval - 2017

ABSTRACT:

Multilabel ranking is an important Machine Learning task with several applications, such as content-based mostly image retrieval (CBIR). But, when the amount of labels is giant, traditional algorithms are either infeasible or show poor performance. During this paper, we propose a easy nonetheless effective multilabel ranking algorithm that is primarily based on k-nearest neighbor paradigm. The proposed algorithm ranks labels consistent with the chances of the label association using the neighboring samples around a question sample. Totally different from traditional approaches, we take solely positive samples into consideration and determine the model parameters by directly optimizing ranking loss measures. We have a tendency to evaluated the proposed algorithm using four common multilabel datasets. The proposed algorithm achieves equivalent or higher performance than other instance-primarily based learning algorithms. When applied to a CBIR system with a dataset of 1 million samples and over one hundred ninety thousand labels, that is abundant larger than any other multilabel datasets used earlier, the proposed algorithm clearly outperforms the competing algorithms.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here