Concept Concept in Images Using SVD Features and MulTi Granularity Partitioning and Classification
سال انتشار: 1396
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 381
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شناسه ملی سند علمی:
JR_JIST-5-3_001
تاریخ نمایه سازی: 20 آبان 1397
چکیده مقاله:
New visual and static features, namely, right singular feature vector, left singular feature vector and singular value feature vector are proposed for the semantic concept detection in images. These features are derived by applying singular value decomposition (SVD) directly to the raw images. In SVD features edge, color and texture information is integrated simultaneously and is sorted based on their importance for the concept detection. Feature extraction is performed in a multi-granularity partitioning manner. In contrast to the existing systems, classification is carried out for each grid partition of each granularity separately. This separates the effect of classifications on partitions with and without the target concept on each other. Since SVD features have high dimensionality, classification is carried out with K-nearest neighbor (K-NN) algorithm that utilizes a new and stable distance function, namely, multiplicative distance. Experimental results on PASCAL VOC and TRECVID datasets show the effectiveness of the proposed SVD features and multi-granularity partitioning and classification method.
کلیدواژه ها:
High-Dimensional Data ، Multi-Granularity Partitioning and Classification ، Multiplicative Distance ، Semantic Concept Detection ، Static Visual Features ، SVD
نویسندگان
Kamran Farajzadeh
Department of IT management, Islamic Azad University, Science and Research Branch, Tehran, Iran
Esmail Zarezadeh
Department of Electrical Engineering, Amir Kabir University, Tehran, Iran
Jafar Mansouri
Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran