A dense subgraph based algorithm for compact salient image region detection

Abstract

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.

A Dense sub-graph based approach for Automatic detection of Optic Disc

“…Image by taking into consideration segmented image area as vertex and aloofness. Graph Base Visual Segmentation united the loads as the product of different loads [12]. Im are mean intensity assessment of the element canal t,where t=1,2,3 for L*,a*,b* consider for nodes m,n correspondingl [12].…”

“…The graph Gim derived from N*N transition matrix , where N is the number of nodes. The element TP(i, j) is proportional to the graph weight w(i, j).The graph Gimage is a diagonal matrix whose degree matrix which is denoted W,the equation is [12]  …”

“…Each column sum of the value in transition matrix must be 1.the transition matrix equation is [12] 1   AW TP (…”

“…This has been realised by taking into consideration an extra informative local graph construction, i.e., a dense sub-graph, rather than easy centrality evaluation in finding the map. A dense k-sub-graph based algorithm for compact salient image region detection [12, 32] using the saliency model. This technique does not provide better results for optic disc location, but we have modified the slic technique and use spdbscan to get better results than only the saliency technique.…”

Salient object detection based on super-pixel clustering and unified low-rank representation

“…Instead of predicting a few fixation points in an image, new saliency detection methods uniformly highlight the entire salient region in the foreground (Achanta and Hemami, 2009, ;Cheng and Mitra, 2015, ;Gong and Tao, 2015, ;Wang et al, 2016, ;Chakraborty and Mitra, 2016, ;Liu and Han, 2016, ;Kim et al, 2016, ;Wei and Wen, 2012). For example, Achanta et al (Achanta and Hemami, (2009) first presented a frequency-tuned based salient region detection method that outputted full resolution saliency maps with well-defined boundaries of salient objects by substantially retaining more spatial frequency contents from the original image.…”