کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
528670 | 869593 | 2014 | 6 صفحه PDF | دانلود رایگان |
• Affinity propagation (AP) clustering is used to extract “visual-words”.
• Minimum and maximum Euclidean distances is used to calculate mapping feature.
• Multiple kernel SVM based MIL algorithm is proposed for image classification.
• The MKSVM-MIL is robust and comparable to other state-of-the-art MIL algorithms.
In this paper, a novel multi-instance learning (MIL) algorithm based on multiple-kernels (MK) framework has been proposed for image classification. This newly developed algorithm defines each image as a bag, and the low-level visual features extracted from its segmented regions as instances. This algorithm is started from constructing a “word-space” from instances based on a collection of “visual-words” generated by affinity propagation (AP) clustering method. After calculating the distance between a “visual-word” and the bag (image), a nonlinear mapping mechanism is introduced for registering each bag as a coordinate point in the “word-space”. In this case, the MIL problem is transformed into a standard supervised learning problem, which allows multiple-kernels support vector machine (MKSVM) classifiers to be trained for the image categorization. Compared with many popular MIL algorithms, the proposed method, named as MKSVM-MIL, shows its satisfactorily experimental results on the COREL dataset, which highlights the robustness and effectiveness for image classification applications.
Journal: Journal of Visual Communication and Image Representation - Volume 25, Issue 5, July 2014, Pages 1112–1117