A bibliography of object class recognition and object recognition based on visual attention

Object class recognition has exhibited significant progress in recent years and is now an integral component of many machine vision applications. However, object class recognition using visual attention image segmentation is a novel idea, which has only been developed in the past decade. This paper presents a comprehensive survey on object class recognition and object recognition algorithms, in addition to their applications based on visual attention region selection methods that as recently published. Additionally, increased efforts have been directed to the development of a generic method for categorizing all objects in a domain including examples such as Winn’s Method, used to recognize object classes at a glance. The Majority of object class recognition algorithms are highly dependent on shape matching results. The purpose of this review is to provide a comparison among the visual attention (bottom-up and top-down), object recognition (e.g., SIFT, SURF and PCA-SIFT) and object class recognition methods, aimed to researchers identifying the most appropriate method for a particular purpose. This survey is suitable for researchers in the pattern recognition field, providing familiarity with the existing algorithms for object classification from image acquisition steps to final output (i.e., image segmentation, object recognition and object classification). At the end of each part, the challenges, critical analysis table are provided and future directions of every method are suggested for developing new ideas end of this paper. Additionally, this approach allows researchers to find the definition of keywords and to obtain brief knowledge concerning how each method works and what obtained results are for various datasets.

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[58]  Wei Zhang,et al.  Object class recognition using multiple layer boosting with heterogeneous features , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[59]  Christof Koch,et al.  Attentional Selection for Object Recognition - A Gentle Way , 2002, Biologically Motivated Computer Vision.

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[61]  Derek Hoiem,et al.  3D LayoutCRF for Multi-View Object Class Recognition and Segmentation , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[62]  Lixin Fan,et al.  Categorizing Nine Visual Classes using Local Appearance Descriptors , 2004 .

[63]  Tingting Xu,et al.  Autonomous Behavior-Based Switched Top-Down and Bottom-Up Visual Attention for Mobile Robots , 2010, IEEE Transactions on Robotics.

[64]  Takio Kurita,et al.  Image representation for generic object recognition using higher-order local autocorrelation features on posterior probability images , 2012, Pattern Recognit..

[65]  Cordelia Schmid,et al.  Semi-Local Affine Parts for Object Recognition , 2004, BMVC.

[66]  Pushmeet Kohli,et al.  On Detection of Multiple Object Instances Using Hough Transforms , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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[68]  Shimon Ullman,et al.  Semantic Hierarchies for Recognizing Objects and Parts , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[69]  Ilkay Ulusoy,et al.  Generative versus discriminative methods for object recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[70]  Jitendra Malik,et al.  Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[71]  Lu Wang,et al.  2D Conditional Random Fields for Image Classification , 2006, Intelligent Information Processing.