Please use this identifier to cite or link to this item: https://doi.org/10.1109/TIP.2013.2278465
Title: Discovering discriminative graphlets for aerial image categories recognition
Authors: Zhang, L.
Han, Y.
Yang, Y.
Song, M.
Yan, S. 
Tian, Q.
Keywords: Aerial image category
Discrimination
Graphlets
Redundancy
Topologies selection
Issue Date: 2013
Source: Zhang, L., Han, Y., Yang, Y., Song, M., Yan, S., Tian, Q. (2013). Discovering discriminative graphlets for aerial image categories recognition. IEEE Transactions on Image Processing 22 (12) : 5071-5084. ScholarBank@NUS Repository. https://doi.org/10.1109/TIP.2013.2278465
Abstract: Recognizing aerial image categories is useful for scene annotation and surveillance. Local features have been demonstrated to be robust to image transformations, including occlusions and clutters. However, the geometric property of an aerial image (i.e., the topology and relative displacement of local features), which is key to discriminating aerial image categories, cannot be effectively represented by state-of-the-art generic visual descriptors. To solve this problem, we propose a recognition model that mines graphlets from aerial images, where graphlets are small connected subgraphs reflecting both the geometric property and color/texture distribution of an aerial image. More specifically, each aerial image is decomposed into a set of basic components (e.g., road and playground) and a region adjacency graph (RAG) is accordingly constructed to model their spatial interactions. Aerial image categories recognition can subsequently be casted as RAG-to-RAG matching. Based on graph theory, RAG-to-RAG matching is conducted by comparing all their respective graphlets. Because the number of graphlets is huge, we derive a manifold embedding algorithm to measure different-sized graphlets, after which we select graphlets that have highly discriminative and low redundancy topologies. Through quantizing the selected graphlets from each aerial image into a feature vector, we use support vector machine to discriminate aerial image categories. Experimental results indicate that our method outperforms several state-of-the-art object/scene recognition models, and the visualized graphlets indicate that the discriminative patterns are discovered by our proposed approach. © 1992-2012 IEEE.
Source Title: IEEE Transactions on Image Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/55661
ISSN: 10577149
DOI: 10.1109/TIP.2013.2278465
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