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ICCV
2005
IEEE
14 years 9 months ago
A Maximum Entropy Framework for Part-Based Texture and Object Recognition
This paper presents a probabilistic part-based approach for texture and object recognition. Textures are represented using a part dictionary found by quantizing the appearance of ...
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
CVPR
2005
IEEE
14 years 1 months ago
Jensen-Shannon Boosting Learning for Object Recognition
In this paper, we propose a novel learning method, called Jensen-Shannon Boosting (JSBoost) and demonstrate its application to object recognition. JSBoost incorporates Jensen-Shan...
Xiangsheng Huang, Stan Z. Li, Yangsheng Wang
BVAI
2007
Springer
14 years 1 months ago
Neural Object Recognition by Hierarchical Learning and Extraction of Essential Shapes
We present a hierarchical system for object recognition that models neural mechanisms of visual processing identified in the mammalian ventral stream. The system is composed of ne...
Daniel Oberhoff, Marina Kolesnik
ICCV
2009
IEEE
13 years 5 months ago
Incremental Multiple Kernel Learning for object recognition
A good training dataset, representative of the test images expected in a given application, is critical for ensuring good performance of a visual categorization system. Obtaining ...
Aniruddha Kembhavi, Behjat Siddiquie, Roland Miezi...
CVPR
2007
IEEE
14 years 9 months ago
Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data
A popular approach to problems in image classification is to represent the image as a bag of visual words and then employ a classifier to categorize the image. Unfortunately, a si...
Liu Yang, Rong Jin, Caroline Pantofaru, Rahul Sukt...