Multi-scale regions from edge fragments a graph theory approach

In this article we introduce a novel method for detecting multi-scale salient regions around edges using a graph based image compression algorithm. Images are recursively decomposed into triangles arranged into a binary tree using linear interpolation. The entropy of any local region of the image is inherent in the areas of the triangles and tree depth. We introduce twin leaves as nodes whose sibling share the same characteristics. Triangles corresponding to the twin leaves are filtered out from the binary tree. Graph connectivity is exploited to get clusters of triangles followed by ellipse fitting to estimate regions. Salient regions are thus formed as stable regions around edges. Tree hierarchy is then used to generate multi-scale regions. We evaluate our detector by performing image retrieval tests on our building database which shows that combined with Spin Images (Lazebnik et al., 2003), their performance is comparable to SIFT (Lowe, 2004). We also show that when they are used together with MSERs (Matas et al., 2002), the performance of MSERs is boosted.