An Image Recognition Method Based on Scene Semantics

Aimed at the image recognition between scenes and objects, we propose a sort of image recognition method based on scene semantics (IRMSS). In IRMSS, the landmark objects of various scenes are collected to form a feature database named Symbolic Objects Database and marked firstly; secondly, the remaining objects in the image could be identified one by one according to scene semantics known from the step forward; and thirdly, the scene of the image would be repeated validated and continuous concreted by using recognition results of each time to form a feedback system for the recognition of image semantics. At final, the simulation experiments showed that IRMSS could sharply promote the accuracy and efficiency of image semantic recognition in the case of strong semantic scene.

[1]  Iasonas Kokkinos,et al.  HOP: Hierarchical object parsing , 2009, CVPR.

[2]  Marcus Hutter,et al.  Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet , 2003, J. Mach. Learn. Res..

[3]  Thomas Hofmann,et al.  Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.

[4]  Andrew Zisserman,et al.  Scene Classification Using a Hybrid Generative/Discriminative Approach , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Frédéric Jurie,et al.  Sampling Strategies for Bag-of-Features Image Classification , 2006, ECCV.

[6]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[8]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[9]  Bernt Schiele,et al.  International Journal of Computer Vision manuscript No. (will be inserted by the editor) Semantic Modeling of Natural Scenes for Content-Based Image Retrieval , 2022 .

[10]  Antonio Criminisi,et al.  Epitomic Location Recognition , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.