Abstract
The success of sparse representations in image modeling and recovery has motivated its use in computer vision applications. Image retrieval and classification tasks require extracting features that discriminate different image classes. State-of-the-art object recognition methods based on sparse coding use spatial pyramid features obtained from dense descriptors. In this paper, we develop a feature extraction method that uses multiple global/local features extracted from large overlapping regions of an image, which we refer to as sub-images. We propose a procedure for dictionary design and supervised local sparse coding of sub-image heterogeneous features. We perform image retrieval on the Microsoft Research Cambridge image dataset and show that the proposed features outperform the spatial pyramid features obtained using dense descriptors.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - International Conference on Image Processing, ICIP |
| Pages | 3117-3120 |
| Number of pages | 4 |
| DOIs | |
| State | Published - 2012 |
| Event | 2012 19th IEEE International Conference on Image Processing, ICIP 2012 - Lake Buena Vista, FL, United States Duration: Sep 30 2012 → Oct 3 2012 |
Other
| Other | 2012 19th IEEE International Conference on Image Processing, ICIP 2012 |
|---|---|
| Country | United States |
| City | Lake Buena Vista, FL |
| Period | 9/30/12 → 10/3/12 |
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Keywords
- dictionary learning
- image retrieval
- Local linear modeling
- Sparse coding
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
Cite this
Supervised local sparse coding of sub-image features for image retrieval. / Thiagarajan, Jayaraman J.; Natesan Ramamurthy, Karthikeyan; Sattigeri, Prasanna; Spanias, Andreas.
Proceedings - International Conference on Image Processing, ICIP. 2012. p. 3117-3120 6467560.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Supervised local sparse coding of sub-image features for image retrieval
AU - Thiagarajan, Jayaraman J.
AU - Natesan Ramamurthy, Karthikeyan
AU - Sattigeri, Prasanna
AU - Spanias, Andreas
PY - 2012
Y1 - 2012
N2 - The success of sparse representations in image modeling and recovery has motivated its use in computer vision applications. Image retrieval and classification tasks require extracting features that discriminate different image classes. State-of-the-art object recognition methods based on sparse coding use spatial pyramid features obtained from dense descriptors. In this paper, we develop a feature extraction method that uses multiple global/local features extracted from large overlapping regions of an image, which we refer to as sub-images. We propose a procedure for dictionary design and supervised local sparse coding of sub-image heterogeneous features. We perform image retrieval on the Microsoft Research Cambridge image dataset and show that the proposed features outperform the spatial pyramid features obtained using dense descriptors.
AB - The success of sparse representations in image modeling and recovery has motivated its use in computer vision applications. Image retrieval and classification tasks require extracting features that discriminate different image classes. State-of-the-art object recognition methods based on sparse coding use spatial pyramid features obtained from dense descriptors. In this paper, we develop a feature extraction method that uses multiple global/local features extracted from large overlapping regions of an image, which we refer to as sub-images. We propose a procedure for dictionary design and supervised local sparse coding of sub-image heterogeneous features. We perform image retrieval on the Microsoft Research Cambridge image dataset and show that the proposed features outperform the spatial pyramid features obtained using dense descriptors.
KW - dictionary learning
KW - image retrieval
KW - Local linear modeling
KW - Sparse coding
UR - http://www.scopus.com/inward/record.url?scp=84875854962&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84875854962&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2012.6467560
DO - 10.1109/ICIP.2012.6467560
M3 - Conference contribution
SN - 9781467325332
SP - 3117
EP - 3120
BT - Proceedings - International Conference on Image Processing, ICIP
ER -