کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6866194 | 679096 | 2015 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel topic feature for image scene classification
ترجمه فارسی عنوان
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
We propose a novel topic feature for image scene classification. The feature is defined based on the thematic representation of images constructed by using topics, i.e., the latent variables of LDA (latent Dirichlet allocation) and their learning algorithms. Different from the related works, the feature defined in this paper shares topics in different classes, and does not need class labels before classification, so that it can avoid the coupling between features and labels. For representing a new image, our approach directly extracts its topic feature by codewords linear mapping instead of the inference of latent variable. We compared our method with three other topic models under similar experimental condition, as well as with pooling methods on the 15 Scenes dataset. The results show that our approach is capable of classifying the scene classes with a higher accuracy than the other topic models and pooling methods without using spatial information. We also observe that the performance improvement is due to the proposed feature and our algorithm, rather than the other factors such as additional low-level image features and stronger preprocessing.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 148, 19 January 2015, Pages 467-476
Journal: Neurocomputing - Volume 148, 19 January 2015, Pages 467-476
نویسندگان
Mujun Zang, Dunwei Wen, Ke Wang, Tong Liu, Weiwei Song,