A dense subgraph based algorithm for compact salient image region detection
This work addresses the problem of compact salient region detection for computer vision applications, but it is incremental as it builds on existing graph-based methods.
The paper tackles the problem of detecting visually salient regions in images by proposing a graph-based algorithm that uses dense subgraphs to enhance saliency maps derived from random walks on a Markov chain. The method achieves performance comparable to well-known algorithms on benchmark datasets.
We present an algorithm for graph based saliency computation that utilizes the underlying dense subgraphs in finding visually salient regions in an image. To compute the salient regions, the model first obtains a saliency map using random walks on a Markov chain. Next, k-dense subgraphs are detected to further enhance the salient regions in the image. Dense subgraphs convey more information about local graph structure than simple centrality measures. To generate the Markov chain, intensity and color features of an image in addition to region compactness is used. For evaluating the proposed model, we do extensive experiments on benchmark image data sets. The proposed method performs comparable to well-known algorithms in salient region detection.