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50120130405009

  1. 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 5, September – October (2013), pp. 67-75 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET ©IAEME A NEW APPROACH TO CONTEXT AWARE SALIENCY DETECTION USING GENETIC ALGORITHM Geetkiran Kaur1 & Parvinder Kaur 2 1 2 M. Tech. Student, Department of Computer Science & Engg., SUSCET, Tangori Assistant Professor, Department of Computer Science & Engg., SUSCET, Tangori ABSTRACT In this paper we present a new saliency detection model which not only detect the the dominant salient object but also the image regions that give some information about the scene. Starting from the initial segmentation result obtained from applying multi iteration multithresholding algorithms, and then applying morphology based edge detection method. The proposed method applies hough transforms to detect the energy content of the image, then genetic algorithms can be expoited to detect the salient region in the image. Keywords: Saliency, Informative saliency, Content based saliency, context aware, Genetic algorithms, Hough transform, multithresholding, computer vision, pattern recognition. 1. INTRODUCTION Saliency detection is the process of detecting the interesting visual information in an image It is fascinating to know that human visual system can adapt to wide range of environmental changes automatically and extract only useful information needed from the complex scence quickly and this has led to a wide spread view that such an efficient information processing capability relates a lot to human attention mechanism The main information to be of concern to the visual system are the brightness of the environment, depth of the object intensity, colour, shape motion etc. In recent years may visual attention models are being proposed although they have different structures and methods but a general conclusion is eached that visual attentional model consist of two aspects bottom up and top down. While bottom up is concern with the sensory data that is, what a observer viewing freely without any bais will notice in a scene. It takes sensory input like brightness, color, motion, depth, orientation of the scene into consideration to predict the attention spotlight, top down aspect deals with the mental 67
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME viewing condition of the viewers like his experiences, state of mind, etc. it relates to reflection of the human activities. At present, a significant number of saliency detection algorithms are developed relevant to bottom up mechanisms some takes local consideration into account i.e brightness, intensity, color, depth etc ,some take global consideration like suppressing the frequency appearing features etc .some use both local and global methods. Most of aims to extracts the most salient object from the visual scence. But when we describe a picture then it is not only the main dominant object but also the immediate surrounding that has to be taken into account to give the complete description of the object .so, what we actually describe is the main object with its context. goferman et.al in [1] introduces the context aware saliency . We propose here a context aware visual model that will use edge information, energy distribution and genetic algorithms to detect the salient objects along with the salient context .we can say it will provide information about the salient object. Genetic algorithm is a search heuristic that mimics the process of natural evolution .this heuristics is routinely used to generate useful solutions to optimization and search problems .In genetic algorithms a population of candidate solutions called individuals creatures or phenotypes to and optimization problems is evolved towards better solutions. Each candidate solution have a set of properties (its chromosomes or genotype) which can be mutated or altered, traditionally solutions are in the forms of ‘0’ and ‘1’. The evolution usually starts from a population of randomly generated individuals and is an iterative process. In each iteration a generation is created. In each generation fitness of every individual is evaluated, the fitness is usually the value of the objective function in the optimization problem being solved. The most fit individuals are selected from the population and each individual genome is modified to form an new generation, the new generation is then used for the next iteration of the algorithm .the algorithm terminates when either a maximum number of generations are produced or a satisfactory fitness level has been reached for the population. 2. RELATED WORK Many visual attention models have been proposed for detecting saliency Zhi liu et.al [2] proposed an image segmentation model aimed at salient object extraction starting from an oversegmented image and performing region merging using a novel dissimilarity measure considering the impact of color difference ,area factor and binary partion tree is generate to record the merging sequence Uvika et.al [3] proposed a morphology based edge detection method and binary partition tree for object extraction using intensity based region merging. Xiangyun hu et.al [4] proposed a simple method for measuring the saliency of texture and object based on the edge density and spatial evenness of the edge distribution in the local window of each pixel. Xuejje Zhang et.al [5] proposed a salient detection model from an oversegmented image. Segment that are similar and spread over the image receive low saliency and segment which is distinct in the whole image or in local region receive high saliency zhenzhong chen et.al [6] proposed hybrid saliency detection using the low level and high level clues imposed by the photographer. shangwang liu et.al [7] proposed an automatic region detection algorithm by extending graph based visual saliency model using pulse coded neural networks (PCNN) to implement the well defined criteria for saliency detection . 68
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME 3. IMPLEMENTATION 3.1 Proposed Algorithm Step 1: Read the RGB image. Fig 1: Input Image Step 2: Extract the red, green and blue components from the image. (a) Red Component (b) Green component (c) Blue Component Fig 2: Extracted Component Step 3: Applying multi thresholding and morphological function on each component. (a) Red component (b) Green Component (c) Blue Component Fig: 3 Thresholded Components of image 69
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME Step 4: Recombining the edges to form the required oversegmented Result Fig 4: Oversegmented Image Step 5: To determine the energy content of the image apply Hough transform Fig 5: Hough Image Step 5: To normalize the energy from Hough generated Image, Maximum energy value is calculated for the Image and then iteratively, Various Energy level are generated in the angle of 155 to 255. Fig 6: Normalized Image 70
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME Step 6: Genetic Algorithm is applied to enhance the energy level of the output image of Step 5. Intensity based fitness function is used for this case. Fig 7: Genetically Enchanced Image Step 7: Output image now contain two parts forground(Salient part)and Background, Intensity of the foreground is increased and Background is Decreased further to improve the result. Fig: 8: Gradient of the image Step 8: Global and Local Centre of focus is determined with the help of energy content and gradeint calculated. Fig 9: Centre of focus 71
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME Step 9: The focus of the gradient is done from the centre of the focus determined in the step 9. To generate the final saliency map Fig 10: Saliency Map 3.2 Flowchart for proposed algorithm Calculate parameter to compute ROC Curve Image loading in Memory Multithresholding for Oversegmentation Output Saliency Map Hough Transform as Energy Detection Cretaria Gradient focus suing from Centre of saliency Normalization of Energy Levels Predicting Centre of Gravity using Gradient Genetic Algorithm for Energy Level Enhancement Increasing the intensity of salient region based on the Energy Content Fig: 11: Flowchart of the Proposed Algorithm 4. EXPERIMENTAL RESULTS We consider that edges are the main source of information for the salient part, taking in consideration the energy density of the edges of the image, we can incorporate both the local and global features, As the region of high energy is the region that get the attention of the human eye .Center of focus can easily be detected by Energy content of the image. We have use Hough transforms, which will divide the image into various energy level and genetic algorithms to enhance the result. 72
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME In Fig12 we compares our results with Local method and Global method where Local method where Fig 12(a) is the input image Fig 12(b) Saliency map from a local method [4], Fig 12(c) Saliency map from a global method [8] .Fig 12(d) depicts our approach Fig: 12 a) Input Image 5. b) Local Method. [4] c) Global Method. [8] d) Our Approach QUANTITATIVE EVALUATION To obtain quantitative evaluation we plot ROC Curve Firstly true positive rate (TPR) and false positive rate (FPR) are calculated on the base of the ground truth database. The TPR defines how many correct positive results occur among all positive results given by the algorithm, FPR, on the other hand, defines how many incorrect positive results occur among all negative results given by the algorithm. 73
  8. 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME A ROC space is defined by FPR and TPR as x and y axes respectively, which depicts relative trade-offs between true positive and false positive. Daigonal divides the the ROC space the points above the diagonal represents good classification results and the points below shows poor results .we have compared our Results with 7 different state of art algorihms [8], [9], [10], [11], [12], [13], [14]. From the roc curve plotted we find that our algorithm outperform the other state of art algorithms as our curve shown in red colour is higher than other algorithms. Fig 13: Receiver Operating Curve 6. CONCLUSION In this paper, we present a new saliency detection method, which not detect the salient object but also the salient background which convey some meaning about the salient object. As shown by the quantitative results, our approach which considers the energy density of the image to calculate the salient regions of the image outperforms most of the present state of art salient detection algorithms. REFERENCES 1. 2. 3. STAS GOFERMAN,LIHI ZELNIK-MANOR,AND AYELLET TAL ,”CONTEXT AWARE SALIENCY”, IEEE TRANSACTION ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE VOL 34, NO .10, OCTOBER 2012 Zhi Liu, Liquan Shen, Zhaoyang Zhang, “Unsupervised Image Segmentation Based on Analysis of Binary Partition Tree for Salient Object Extraction”, ELSEVIER, Signal Processing 91 (2011) pp. 290-299. Uvika and Kaur Sumeet,” Image Segmentation and Object Extraction using Binary Partition Tree “, IJCSC.Vol-8 no.1, January –June 2012. pp 147-150. 74
  9. 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. Xiangyun Hu, Jiajie Shen, Jie Shan, and Li Pan “Local Edge Distributions for Detection of Salient Structure Textures and Objects” IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 10, NO. 3, MAY 2013. Xuejie Zhang, Zhixiang Ren, Deepu Rajan, Yiqun Hu “Salient Object Detection through OverSegmentation” 2012 IEEE International Coference on Multimedia and Expo. Zhenzhong Chen, Junsong Yuan, and Yap-Peng Tan “Hybrid Saliency Detection for Images” IEEE SIGNAL PROCESSING LETTERS, VOL. 20, NO. 1, JANUARY 2013. Shangwang Liu, Dongjian He, and Xinhong Liang “An Improved Hybrid Model for Automatic Salient Region Detection” Proceedings of the 2012 International Conference on Wavelet Analysis and Pattern Recognition, Xian, 15-17 July, 2012. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, “Frequency Tuned Salient Region Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1597-1604, 2009. C. Guo, Q. Ma, and L. Zhang, “Spatio-Temporal Saliency Detection Using Phase Spectrum of Quaternion Fourier Transform,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, (pp. 1-8, 2008) J. Harel, C. Koch, and P. Perona, “Graph-Based Visual Saliency,” Advances in Neural Information Processing Systems, vol. 19, pp. 545- 552, 2007. X. Hou and L. Zhang, “Saliency Detection: A Spectral Residual Approach,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2007. L. Itti, C. Koch, and E. Niebur, “A Model of Saliency-Based Visual Attention for Rapid Scene Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 12541259, Nov. 1998. T. Judd, K. Ehinger, F. Durand, and A. Torralba, “Learning to Predict Where Humans Look,” Proc. IEEE Int’l Conf. Computer Vision, pp. 2106-2113, 2009. E. Rahtu, J. Kannala, M. Salo, and J. Heikkila¨, “Segmenting Salient Objects from Images and Videos,” Proc. 11th European Conf. Computer Vision, pp. 366-379, 2010. Prof. S.V.M.G.Bavithiraja and Dr.R.Radhakrishnan, “Power Efficient Context-Aware Broadcasting Protocol for Mobile Ad Hoc Network”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 81 - 96, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. Shameem Akthar, Dr. D Rajaylakshmi and Dr. Syed Abdul Sattar, “A Modified Pso Based Graph Cut Algorithm for the Selection of Optimal Regularizing Parameter in Image Segmentation”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 3, 2013, pp. 273 - 279, ISSN Print: 0976-6480, ISSN Online: 0976-6499. Gaganpreet Kaur and Dr. Dheerendra Singh, “Pollination Based Optimization for Color Image Segmentation”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 407 - 414, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 75

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