Context-aware affective images classification based on bilayer sparse representation
Proceedings of the 20th ACM international conference on Multimedia
Scaring or pleasing: exploit emotional impact of an image
Proceedings of the 20th ACM international conference on Multimedia
Horror video scene recognition based on multi-view multi-instance learning
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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Along with the ever-growing Web, horror contents sharing in the Internet has interfered with our daily life and affected our, especially children's, health. Therefore horror image recognition is becoming more important for web objectionable content filtering. This paper presents a novel context-aware multi-instance learning (CMIL) model for this task. This work is distinguished by three key contributions. Firstly, the traditional multi-instance learning is extended to context-aware multi-instance learning model through integrating an undirected graph in each bag that represents contextual relationships among instances. Secondly, by introducing a novel energy function, a heuristic optimization algorithm based on Fuzzy Support Vector Machine (FSVM) is given out to find the optimal classifier on CMIL. Finally, the CMIL is applied to recognize horror images. Experimental results on an image set collected from the Internet show that the proposed method is effective on horror image recognition.