Selective Search for Object Recognition

This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustiv...

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Veröffentlicht in:	International journal of computer vision Jg. 104; H. 2; S. 154 - 171
Hauptverfasser:	Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., Smeulders, A. W. M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston Springer US 01.09.2013 Springer Springer Nature B.V
Schlagworte:	Applied sciences Artificial Intelligence Computer Imaging Computer programs Computer Science Computer science; control theory; systems Datasets Exact sciences and technology Image Processing and Computer Vision Image processing systems Information management Localization Object recognition Pattern Recognition Pattern Recognition and Graphics Pattern recognition. Digital image processing. Computational geometry Recall Sampling Search engines Searching Segmentation Software Studies Vision United States > US Object Recognition Colour Space Object Location Exhaustive Search Appearance Model Computer vision Bag of words Segmentation Information retrieval Data driven modelling Pattern recognition Sampling Overlay Object recognition Artificial intelligence Modeling Object location
ISSN:	0920-5691, 1573-1405
Online-Zugang:	Volltext
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Abstract	This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html ).
AbstractList	This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html ). This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html). This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html).[PUBLICATION ABSTRACT] This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software:
Audience	Academic
Author	Gevers, T. Uijlings, J. R. R. Smeulders, A. W. M. van de Sande, K. E. A.
Author_xml	– sequence: 1 givenname: J. R. R. surname: Uijlings fullname: Uijlings, J. R. R. email: jrr.uijlings@gmail.com, jrr@disi.unitn.it organization: University of Trento – sequence: 2 givenname: K. E. A. surname: van de Sande fullname: van de Sande, K. E. A. organization: University of Amsterdam – sequence: 3 givenname: T. surname: Gevers fullname: Gevers, T. organization: University of Amsterdam – sequence: 4 givenname: A. W. M. surname: Smeulders fullname: Smeulders, A. W. M. organization: University of Amsterdam
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Snippet	This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the...
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SubjectTerms	Applied sciences Artificial Intelligence Computer Imaging Computer programs Computer Science Computer science; control theory; systems Datasets Exact sciences and technology Image Processing and Computer Vision Image processing systems Information management Localization Object recognition Pattern Recognition Pattern Recognition and Graphics Pattern recognition. Digital image processing. Computational geometry Recall Sampling Search engines Searching Segmentation Software Studies Vision
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Title	Selective Search for Object Recognition
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