Name
Abstract | ||
|---|---|---|
In Bag-of-Words BoW based image retrieval, soft assignment SA assigns R-nearest visual words to a feature, which significantly enhances the performance of image retrieval. However, it requires to calculate the weight of each visual word according to the distance from feature to visual word. This method sometimes loses its power when the codebook size is small, since a smaller codebook will cause larger quantization error and lead the distance to be more imprecise. In this paper, instead of depending on distance, we present a novel method to calculate the similarity between features by counting the number of identical visual words assigned to them. We describe how to create the inverted index, and weight the score between matched features. We evaluate the proposed Multi-Matched Similarity MMS method on Holidays and Ukbench datasets. Experimental results demonstrate that our method significantly improves the retrieval performance and outperforms the SA approach on both datasets. |
Year | Venue | Field |
|---|---|---|
2016 | HCC | Inverted index,Pattern recognition,Computer science,Image retrieval,Artificial intelligence,Quantization (signal processing),Visual Word,Codebook |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
11 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhun Zhong | 1 | 12 | 1.58 |
| Li-Chuan Geng | 2 | 0 | 0.68 |
| Song-zhi Su | 3 | 61 | 8.53 |
| Guo-xi Wu | 4 | 0 | 0.68 |
| Shaozi Li | 5 | 403 | 54.27 |