Negative-voting and class ranking based on local discriminant embedding for image retrieval

In this paper, we propose a novel image retrieval system by using negative-voting and class ranking schemes to find similar images for a query image. In our approach, the image features are projected onto a new feature space that maximizes the precision of image retrieval. The system involves learning a projection matrix for local discriminant embedding, generating class ordering distribution from a negative-voting scheme, and providing image ranking based on class ranking comparison. The evaluation of mean average precision (mAP) on the Holidays dataset shows that the proposed system outperforms the existing retrieval systems. Our methodology significantly improves the image retrieval accuracy by combining the idea of negative-voting and class ranking under the local discriminant embedding framework.

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