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Personal Homepage: http://ttic.uchicago.edu/~dparikh/
Office: Hamerschlag Hall D-level
1111 Hamerschlag Hall
Carnegie Mellon University,
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Low-level Contextual Patch Saliency
Context has been extensively explored for higher level tasks such as recognition. In this work we explore the use of context for a low-level task of determining patch saliency.
Existing saliency measures include interest-points based and discriminative criteria. We define saliency of a patch based on consistency of the patch in context of the rest of the image. We propose occurrence as well as location based contextual saliency measures. We also explore three different strategies to sample patches from a saliency map.
We evaluate our proposed contextual saliency measure by comparing it to existing saliency measures for the tasks of object and scene recognition.
Please refer to Low-level Contextual Patch Saliency for more details.
From Appearance to Context Based Recognition
One can imagine that contextual information is necessary for accurate recognition in low resolution images. However, the same is not the case for high resolution images.
To verify this, in addition to automatic machine recognition, we perform recognition tests in human studies as well.
In this paper, we explore the role of context for dense scene labeling in small images. We promote the scenario with impoverished appearance information as an appropriate venue for studying the role of context in recognition.
Please refer to From Appearance to Context Based Recognition for more details.
Hierarchical Semantics of Objects (hSO)
We introduce hSOs: Hierarchical Semantics of Objects. The hSO captures the interactions between the objects that tend to co-occur in the scene, and hence are potentially semantically related. The proposed approach is entirely unsupervised and can detect the parts of the images that belong to the foreground objects, cluster these parts to represent objects, and provide an understanding of the scene by hierarchically clustering these objects in a semantically meaningful way - all from a collection of unlabeled images of a particular scene category. In addition to providing the semantic layout of the scene, learnt hSOs can have several useful applications such as compact scene representation for scene category classification and providing context for enhanced object detection.
Please refer to Hierarchical Semantics of Objects (hSO) for more details.
Pattern Recognition Techniques for Intrusion Detection Systems
We propose a pattern recognition based novel strategy for adaptive intrusion detection that can evolve with changing network environments. We exploit the ensemble of classifiers approach to combine information from multiple sources and tune the system towards minimizing the cost of the errors. We also propose a novel transform that can map the outputs of different classifiers to common grounds for more meaningful comparison/combination.
Please refer to Pattern Recognition Techniques for Intrusion Detection Systems for more details.
We propose a local feature-based approach to determine compatibility between parts for 3D reassembly. We use a spectral technique to compute the compatibility score between parts.
Please refer to 3D reassembly for more details.
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D. Parikh, and T. Chen. Unsupervised Modeling of Objects and their Hierarchical Contextual Interactions. EURASIP Journal on Image and Video Processing, Special Issue on Patches in Vision, 2008.
D. Parikh, and T. Chen. Data Fusion and Cost Minimization for Intrusion Detection. IEEE Transactions on Information Forensics and Security, Special Issue on Statistical Methods for Network Security and Forensics, August 2008.
R. Polikar, A. Topalis, D. Parikh, D. Green, J. Kounios, and C. Clark. An Ensemble Based Data Fusion for Early Diagnosis of Alzheimer’s Disease. Information Fusion, Special Issue on Applications of Ensemble Methods, January 2008.
D. Parikh, and R. Polikar. An Ensemble Based Incremental Learning Approach to Data Fusion. IEEE Transactions on Systems, Man and Cybernetics, April 2007.
Y. Mehta, K. Jahan, J. Laicovsky, L. Miller, D. Parikh, and A. Lozano. Evaluate the Effect of Coarse and Fine Rubber Particles on Laboratory Rutting Performance of Asphalt Concrete Mixtures. The Journal of Solid Waste Technology And Management, 2005.
C. Mao, H. Lee, D. Parikh, T. Chen and S. Huang. Semi-Supervised Cotraining and Active Learning based Approach for Multi-view Intrusion Detection. ACM Symposium on Applied Computing (SAC), 2009.
D. Parikh, L. Zitnick, and T. Chen. Determining Patch Saliency Using Low-Level Context, European Conference on Computer Vision (ECCV), 2008. [project] [poster presentation].
D. Parikh, L. Zitnick, and T. Chen. From Appearance to Context-Based Recognition: Dense Labeling in Small Images, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008. [project] [poster presentation].
D. Parikh, and T. Chen. Bringing Diverse Classifiers to Common Grounds: dtransform, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2008 [project] [oral presentation].
D. Parikh, and G. Jancke. Localization and Segmentation of a 2D High Capacity Color Barcode, Workshop on Applications in Computer Vision (WACV), 2008 [oral presentation]
D. Parikh, and T. Chen. Unsupervised Identification of Multiple Objects of Interest from Multiple Images: dISCOVER, Asian Conference in Computer Vision (ACCV), 2007. [poster presentation] [project].
D.Parikh, and T. Chen. Classification-Error Cost Minimization Strategy: dCMS. IEEE Statistical Signal Processing Workshop, 2007 [poster presentation] [project].
D.Parikh, and T. Chen. Unsupervised Learning of Hierarchical Semantics of Objects (hSOs). Beyond Patches Workshop, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007. (Best Paper Award) [oral presentation] [project]
D. Parikh, R. Sukthankar, T. Chen, and M. Chen. Feature-based Part Retrieval for Interactive 3D Reassembly. IEEE Workshop on Applications of Computer Vision (WACV), 2007 [poster presentation] [final internship talk at Intel] [project].
R. Polikar, D. Parikh, and S. Mandayam. Multiple Classifiers System for Multisensor Data Fusion. IEEE Proceedings on Sensors Applications Symposium, 2006 [project].
D. Parikh, N. Stepenosky, A. Topalis, D. Green, J. Kounios, C. Clark, and R.Polikar. Ensemble Based Data Fusion for Early Diagnosis of Alzheimer’s Disease. IEEE Proceedings on The Engineering in Medicine and Biology, 2005 [project].
D. Parikh, and R. Polikar. A Multiple Classifier Approach for Multisensor Data Fusion. IEEE Proceedings on Information Fusion, 2005 [project].
D. Parikh, M. Kim, J. Oagaro, S.Mandayam, and R.Polikar. Ensemble of Classifiers Approach for NDT Data Fusion. IEEE Proceedings on Ultrasonics, Ferroelectrics and Frequency Control, 2004 [project].
D. Parikh, Y. Mehta, and K. Jahan. Evaluate the Effect of Ground Tire Rubber on Laboratory Rutting Performance of Asphalt Concrete Mixtures. Proceedings of Industrial and Hazardous Waste Conference, 2002.
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