Bag of visual words based approach for the classification of benign and malignant masses in mammograms using voting-based feature encoding

Object Classification of Aerial Images With Bag-of-Visual Words

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2009.2035644 ◽

2010 ◽

Vol 7 (2) ◽

pp. 366-370 ◽

Cited By ~ 121

Author(s):

Sheng Xu ◽

Tao Fang ◽

Deren Li ◽

Shiwei Wang

Keyword(s):

Object Classification ◽

Aerial Images ◽

Bag Of Visual Words ◽

Visual Words

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Comparison of Deep Learning, Data Augmentation and Bag of-Visual-Words for Classification of Imbalanced Image Datasets

Communications in Computer and Information Science - Recent Trends in Image Processing and Pattern Recognition ◽

10.1007/978-981-13-9181-1_49 ◽

2019 ◽

pp. 561-571 ◽

Cited By ~ 4

Author(s):

Manisha Saini ◽

Seba Susan

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Bag Of Visual Words ◽

Visual Words ◽

Learning Data ◽

Image Datasets

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A Generalized BoVW Model for Content-Based Image Retrieval

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.496-500.1817 ◽

2014 ◽

Vol 496-500 ◽

pp. 1817-1820

Author(s):

Wang Ming Xu ◽

Hang Yang ◽

Kang Ling Fang ◽

Xin Hai Liu

Keyword(s):

Image Retrieval ◽

Sparse Coding ◽

Spectral Clustering ◽

Clustering Algorithms ◽

Bag Of Visual Words ◽

Clustering Methods ◽

Visual Words ◽

Feature Encoding ◽

Visual Dictionary ◽

Computer Vision Applications

BoVW (Bag of Visual Words) Model has attracted much attention for many computer vision applications in which an image is represented by a histogram of visual words. Two of its critical steps are to construct a visual dictionary and to quantize each local feature to its nearest visual word in the dictionary. In this paper, we present the framework of a generalized BoVW (GBoVW) Model in which feature quantization can be replaced by sparse coding based feature encoding. We also propose to use spectral clustering to construct a visual dictionary to overcome the shortcomings of K-Means based clustering algorithms. Image retrieval experiments on ZuBud database indicate that GBoVW Model improves BoVW Model and the visual dictionary generated by spectral clustering achieves better performance than that by K-Means based clustering methods.

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