Image retrieval based on LRGA algorithm and relevance feedback for insect identification
Susumu Gerund, Takahiro Ogawa, Miki Haseyama · 2017
This paper presents an image retrieval method based on local regression and global alignment (LRGA) algorithm and relevance feedback for insect identification. Based on LRGA algorithm, the proposed method enables estimation of ranking scores for image retrieval in such a way that the neighborhood structure of the database can be optimally preserved. This is the biggest contribution of this paper. Then our method measures relevance between the query image and all the images in the database and realizes retrieval of images based on the measured relevance. Furthermore, if positively labeled images obtained by a user are available, they are used as the query relevance information for the relevance feedback to improve the retrieval results. Experimental results show the effectiveness of our method.