Calculating probability of objectness likelihood model based on superpixels

Ji Chao; Liu Huiying; Sun Jingfeng; He Sheng; Huang Minzhu

Establishing calculation model on the probability of saliency objectness likelihood based on superpixels was introduced to dectect image saliency. At first, factors which affected the size of saliency were analyzed according to the principle of saliency and natural characteristics; And then the SLIC algorithm was used to divide image into K superpixels; Next, according to the texture, color and gradient feature information, calculation models were established on probability saliency object under different rules: including compactness in class, color spatial distribution estimation and edge continuity; Moreover,integrating the probability of saliency object under each rule to get the probability of objectness likelihood according to the characteristics of combining with activity in cells responding to stimuli and exponential function; Finally some experiments extracting regions of interest from complex scenes prove that the proposed algorithm is more efficient than other algorithms.

1. School of Automation,Northwestern Polytechnical University,Xi'an 710072,China

Received Date: 2013-03-10

Rev Recd Date: 2013-04-25

Publish Date: 2013-11-25

Key words:

Abstract: Establishing calculation model on the probability of saliency objectness likelihood based on superpixels was introduced to dectect image saliency. At first, factors which affected the size of saliency were analyzed according to the principle of saliency and natural characteristics; And then the SLIC algorithm was used to divide image into K superpixels; Next, according to the texture, color and gradient feature information, calculation models were established on probability saliency object under different rules: including compactness in class, color spatial distribution estimation and edge continuity; Moreover,integrating the probability of saliency object under each rule to get the probability of objectness likelihood according to the characteristics of combining with activity in cells responding to stimuli and exponential function; Finally some experiments extracting regions of interest from complex scenes prove that the proposed algorithm is more efficient than other algorithms.

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Calculating probability of objectness likelihood model based on superpixels

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