This report consists of a literature review of papers dealing with visual recognition using different techniques. Several papers that brought contribution to this field are summarized, analysed and compared. Different papers uses different moreover similar concepts for image/object recognition and their work brought average results in this field. By using the novel concept of Object Bank (OB) very good progress over image/object recognition has been done over recent years. Here we are stipulating the concept of Object Bank for high level visual recognition by using different Support Vector Machine (SVM) classifiers.

Object Bank, Image recognition, Image representation, SVM, Semantic information, Feature extraction, Maximum Entropy

[1]. Felzenszwalb, P., Girshick, R., McAllester, D., & Ramanan, "Object detection with discriminatively trained part based models". Journal of Artificial Intelligence Research, 29, 2007.
[2]. Ferrari, V.,&Zisserman. "Learning visual attributes". In NIPS. ,2007.
[3]. Zhu, L., Chen, Y., & Yuille,. "Unsupervised learning of a probabilistic grammar for object detection and parsing". Advances in neural information processing systems, 19, 1617. 2007.
[4]. Fei-Fei, L., Fergus,R.,&Torralba, A. "Recognizing and learning object categories", 2007.
[5]. Somprasertsri, G.; Lalitrojwong, P., "A maximum entropy model for product feature extraction in online customer reviews," Cybernetics and Intelligent Systems, 2008 IEEE Conference on , vol., no., pp.575,580, 21-24 Sept. 2008.
[6]. Kittikhun Meethongjan, Dzulkifli Mohamad "Maximum Entropy-based Thresholding algorithm for Face image segmentation".,2009.
[7]. Lampert, C. H., Nickisch, H., & Harmeling, S. "Learning to detect unseen by between-class attribute transfer". In CVPR, 2009.
[8]. Felzenszwalb, Girschick, McAllester, "Cascade Object Detection with Deformable Part Models".,2009.
[9]. Torresani, L., Szummer, M., & Fitzgibbon, A. "Efficient object category recognition using classemes". In ECCV.,2010.
[10]. Farhadi, A., Endres, I., & Hoiem, D. "Attribute-centric recognition for cross-category generalization". In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, (pp. 2352–2359). New York: IEEE.
[11]. Torresani, L., Szummer, M., & Fitzgibbon, A. "Efficient object category recognition using classemes". In ECCV.,2010.
[12]. Song, Z., Chen, Q., Huang, Z., Hua, Y., & Yan, S. "Contextualizing object detection and classification". In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).,2011.
[13]. Dixit, M., Rasiwasia, N., & Vasconcelos, N. "Adapted Gaussian models for image classification". In CVPR, 2011.
[14]. Hossein Mobahi, Shankar R. Rao, Allen Y. Yang, Shankar S. Sastry, Yi Ma. "Segmentation of Natural Images by Texture and Boundary Compression". IJCV 2011.
[15]. Tim Althoff, Hyun Oh Song, Trevor Darrell. "Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition"., 2011.
[16]. Michael Stark, Robert Patrick, James roch:"Fine-Grained Categorization for 3D Scene understanding" (BMVC 2012).
[17]. Li-Jia Li • Hao Su • Yongwhan Lim • Li Fei-Fei. "Object Bank: An Object-Level Image Representation for High-Level Visual Recognition". International Journal of Computer Vision, 2013.(pp. 630-660).
[18]. Hao su, Li-Jia Li, Yongwhan Lim, Li Fei-Fei:"Objects as Attributes for Scene Classification". ICCV, 2013.
[19]. Hetal J. Vala, Astha Baxi.: "A Review on Otsu Image Segmentation Algorithm": IJARCET, Volume 2, Issue 2, February 2013.
[20]. Ahmed Bassiouny, Motaz El-Saban:"Semantic Segmentation As Image Representation For Scene Recognition". Microsoft Advanced Technology Labs, Cairo, Egypt.
[21]. en.wikipedia.org/wiki/Support_vector_machine.
[22]. Digital Image Processing 2nd Edition by Gonzalez and Woods Pearson Publications.