A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern

详细信息    查看全文

• 作者：Davar Giveki^a ; ^{Giveki@students.irandoc.ac.ir} ; Mohammad Ali Soltanshahi^b ; ^{ali.soltanshahi@gmail.com} ; Gholam Ali Montazer^c ; ^{montazer@modares.ac.ir}
• 关键词：Content based image retrieval ; SIFT ; HOG ; LBP ; LTP ; LDT
• 刊名：Optik - International Journal for Light and Electron Optics
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：131
• 期：Complete
• 页码：242-254
• 全文大小：3124 K
• 卷排序：131

文摘

This paper presents a new methodology to retrieve images of different scenes by introducing a novel image descriptor. The proposed descriptor works with Scale Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Local Derivative Pattern (LDP), Local Ternary Pattern (LTP) and any other feature descriptor that can be applied on the image pixels. As the proposed descriptor considers a group of pixels together, higher level of semantic is achieved. In this work, a new image descriptor using SIFT and LDP is introduced that is able to find similarities and matches between images. The proposed descriptor produces highly discriminative features for describing image content. Four image datasets are used for evaluating our proposed descriptor. Comprehensive experiments have been conducted using various classifiers and different image features to show the superiority of the proposed method.