SOD-CHIEF: Salient Object Detection using Convex Hull with multI-scale Energy Functions

In the era of multimedia, the process of detection of salient object has emerged as an interesting research area in the field of image processing and computer vision. However, the existing SOD models are unable to make the strike balance in the trade-off between the accuracy of object detection and the computational time required by humans, In this work the authors addresses the above concerns and focuses to improve the SOD accuracy by considering the requirement of less computation time using the Convex Hull with multi-scale energy functions. Experimental analysis of the proposed technique has been done with the measurement parameters of computational time, F-measure, recall and precision on three publicly available image datasets. The computation time of the SOD-CHIEF model is comparatively less than most of the existing techniques with better accuracy for detecting the Salient object.

[1]  Vinod Kumar,et al.  An Automated Hierarchical Framework for Player Recognition in Sports Image , 2017, ICVIP.

[2]  Piyush Kumar,et al.  A-PNR: Automatic Plate Number Recognition , 2017, ICCCT-2017.

[3]  R. K. Agrawal,et al.  Performance enhancement of salient object detection using superpixel based Gaussian mixture model , 2018, Multimedia Tools and Applications.

[4]  Navjot Singh,et al.  Key-Lectures: Keyframes Extraction in Video Lectures , 2018, Advances in Intelligent Systems and Computing.

[5]  Deepti D. Shrimankar,et al.  Deep Event Learning boosT-up Approach: DELTA , 2018, Multimedia Tools and Applications.

[6]  Krishan Kumar,et al.  F-DES: Fast and Deep Event Summarization , 2017, IEEE Transactions on Multimedia.

[7]  Ali Borji,et al.  State-of-the-Art in Visual Attention Modeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  R. K. Agrawal,et al.  A novel approach to combine features for salient object detection using constrained particle swarm optimization , 2014, Pattern Recognit..

[9]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[10]  Laurent Itti,et al.  Saliency and Gist Features for Target Detection in Satellite Images , 2011, IEEE Transactions on Image Processing.

[11]  Krishan Kumar,et al.  D-CAD: Deep and Crowded Anomaly Detection , 2017, ICCCT-2017.

[12]  Nanning Zheng,et al.  Learning to Detect a Salient Object , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Deepti D. Shrimankar,et al.  Eratosthenes sieve based key-frame extraction technique for event summarization in videos , 2018, Multimedia Tools and Applications.

[14]  Xiaochun Cao,et al.  Cluster-Based Co-Saliency Detection , 2013, IEEE Transactions on Image Processing.

[15]  Ali Borji,et al.  Salient Object Detection: A Benchmark , 2015, IEEE Transactions on Image Processing.

[16]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[17]  Patrick Le Callet,et al.  A coherent computational approach to model bottom-up visual attention , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Wenbin Zou,et al.  Saliency Tree: A Novel Saliency Detection Framework , 2014, IEEE Transactions on Image Processing.

[19]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[20]  Huchuan Lu,et al.  Bayesian Saliency via Low and mid Level Cues , 2022 .

[21]  R. K. Agrawal,et al.  Combination of Kullback–Leibler divergence and Manhattan distance measures to detect salient objects , 2015, Signal Image Video Process..

[22]  Deepti D. Shrimankar,et al.  Equal Partition Based Clustering Approach for Event Summarization in Videos , 2016, 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).

[23]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[24]  Anurag Kumar,et al.  A novel superpixel based color spatial feature for salient object detection , 2017, 2017 Conference on Information and Communication Technology (CICT).

[25]  Navjot Singh,et al.  SOMES: An Efficient SOM Technique for Event Summarization in Multi-view Surveillance Videos , 2018 .

[26]  Krishan Kumar,et al.  ESUMM: Event SUMMarization on Scale-Free Networks , 2018, IETE Technical Review.

[27]  R. K. Agrawal,et al.  A novel position prior using fusion of rule of thirds and image center for salient object detection , 2016, Multimedia Tools and Applications.

[28]  Navjot Singh,et al.  Event BAGGING: A novel event summarization approach in multiview surveillance videos , 2017, 2017 International Conference on Innovations in Electronics, Signal Processing and Communication (IESC).