Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Review of the State of the Art in Semantic Scene Classification
Review of the State of the Art in Semantic Scene Classification
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Multimedia surveillance systems
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
Synthesis of interest point detectors through genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A fast learning algorithm for deep belief nets
Neural Computation
Image annotation by large-scale content-based image retrieval
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic programming for image analysis
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Biologically inspired feature manifold for scene classification
IEEE Transactions on Image Processing
CENTRIST: A Visual Descriptor for Scene Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining flickr landmarks by modeling reconstruction sparsity
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Interest point detection through multiobjective genetic programming
Applied Soft Computing
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Real-world scene recognition has been one of the most challenging research topics in computer vision, due to the tremendous intraclass variability and the wide range of scene categories. In this paper, we successfully apply an evolutionary methodology to automatically synthesize domain-adaptive holistic descriptors for the task of scene recognition, instead of using hand-tuned descriptors. We address this as an optimization problem by using multi-objective genetic programming (MOGP). Specifically, a set of primitive operators and filters are first randomly assembled in theMOGP framework as tree-based combinations, which are then evaluated by two objective fitness criteria i.e., the classification error and the tree complexity. Finally, the best-so-far solution selected by MOGP is regarded as the (near-)optimal feature descriptor for scene recognition. We have evaluated our approach on three realistic scene datasets: MIT urban and nature, SUN and UIUC Sport. Experimental results consistently show that our MOGP-generated descriptors achieve significantly higher recognition accuracies compared with state-of-the-art hand-crafted and machine-learned features.