NDLI: Discriminative Cellets Discovery for Fine-Grained Image Categories Retrieval

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Discriminative Cellets Discovery for Fine-Grained Image Categories Retrieval

Content Provider	ACM Digital Library
Author	Zimmermann, Roger Yang, Yi Zhang, Luming
Abstract	Fine-grained image categories recognition is a challenging task aiming at distinguishing objects belonging to the same basic-level category, such as leaf or mushroom. It is a useful technique that can be applied for species recognition, face verification, and etc. Most of the existing methods have difficulties to automatically detect discriminative object components. In this paper, we propose a new fine-grained image categorization model that can be deemed as an improved version spatial pyramid matching (SPM). Instead of the conventional SPM that enumeratively conducts cell-to-cell matching between images, the proposed model combines multiple cells into cellets that are highly responsive to object fine-grained categories. In particular, we describe object components by cellets that connect spatially adjacent cells from the same pyramid level. Straightforwardly, image categorization can be casted as the matching between cellets extracted from pairwise images. Toward an effective matching process, a hierarchical sparse coding algorithm is derived that represents each cellet by a linear combination of the basis cellets. Further, a linear discriminant analysis (LDA)-like scheme is employed to select the cellets with high discrimination. On the basis of the feature vector built from the selected cellets, fine-grained image categorization is conducted by training a linear SVM. Experimental results on the Caltech-UCSD birds, the Leeds butterflies, and the COSMIC insects data sets demonstrate our model outperforms the state-of-the-art. Besides, the visualized cellets show discriminative object parts are localized accurately.
Starting Page	57
Ending Page	64
Page Count	8
File Format	PDF
ISBN	9781450327824
DOI	10.1145/2578726.2578736
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-04-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Categories retrieval Fine-grained Spatial pyramid Sparse coding Cellets
Content Type	Text
Resource Type	Article

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