Con-text: text detection using background connectivity for fine-grained object classification

This paper focuses on fine-grained classification by detecting photographed text in images. We introduce a text detection method that does not try to detect all possible foreground text regions but instead aims to reconstruct the scene background to eliminate non-text regions. Object cues such as color, contrast, and objectiveness are used in corporation with a random forest classifier to detect background pixels in the scene. Results on two publicly available datasets ICDAR03 and a fine-grained Building subcategories of ImageNet shows the effectiveness of the proposed method.

[1]  Gary R. Bradski,et al.  A codebook-free and annotation-free approach for fine-grained image categorization , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Kai Wang,et al.  End-to-end scene text recognition , 2011, 2011 International Conference on Computer Vision.

[3]  Hsueh-Cheng Wang,et al.  The Attraction of Visual Attention to Texts in Real-World Scenes: Are Chinese Texts Attractive to Non-Chinese Speakers? , 2011, CogSci.

[4]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[5]  Jan C. van Gemert,et al.  Exploiting photographic style for category-level image classification by generalizing the spatial pyramid , 2011, ICMR.

[6]  Andrew Zisserman,et al.  A Visual Vocabulary for Flower Classification , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[7]  Theo Gevers,et al.  Object Reading: Text Recognition for Object Recognition , 2012, ECCV Workshops.

[8]  Alain Trémeau,et al.  Detecting Text in Natural Scenes Based on a Reduction of Photometric Effects: Problem of Color Invariance , 2011, CCIW.

[9]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[10]  Alain Trémeau,et al.  Detecting Text in Natural Scenes Based on a Reduction of Photometric Effects: Problem of Text Detection , 2011, CCIW.

[11]  Joost van de Weijer,et al.  Boosting color saliency in image feature detection , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Jiřı́ Matas,et al.  Real-time scene text localization and recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Yonatan Wexler,et al.  Detecting text in natural scenes with stroke width transform , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Alain Trémeau,et al.  A Novel Algorithm for Text Detection and Localization in Natural Scene Images , 2010, 2010 International Conference on Digital Image Computing: Techniques and Applications.

[15]  C. V. Jawahar,et al.  Top-down and bottom-up cues for scene text recognition , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Thomas Deselaers,et al.  Measuring the Objectness of Image Windows , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Derek Hoiem,et al.  Building text features for object image classification , 2009, CVPR.

[18]  Mei-Chen Yeh,et al.  Multimodal fusion using learned text concepts for image categorization , 2006, MM '06.

[19]  Jian Sun,et al.  Geodesic Saliency Using Background Priors , 2012, ECCV.

[20]  Huizhong Chen,et al.  Combining image and text features: a hybrid approach to mobile book spine recognition , 2011, ACM Multimedia.

[21]  Yaokai Feng,et al.  A Keypoint-Based Approach toward Scenery Character Detection , 2011, 2011 International Conference on Document Analysis and Recognition.