Instance Retrieval Using Region of Interest Based CNN Features

• 作者: Ching-nung Yang Jingcheng Chen Zhaoqing Pan Zhili Zhou
• 来源期刊: 新媒体杂志(英文)
• 学科: 工学
• 关键词: Image RETRIEVAL instance RETRIEVAL ROI CNN convolutional layer convolutional FEATURE MAPS
• 年，卷（期）: 2019,（2）
• 页码范围: 87-99
• 页数: 13页
• 分类号: TP3

摘要

Recently, image representations derived by convolutional neural networks(CNN) have achieved promising performance for instance retrieval, and they outperformthe traditional hand-crafted image features. However, most of existing CNN-based featuresare proposed to describe the entire images, and thus they are less robust to backgroundclutter. This paper proposes a region of interest (RoI)-based deep convolutionalrepresentation for instance retrieval. It first detects the region of interests (RoIs) from animage, and then extracts a set of RoI-based CNN features from the fully-connected layerof CNN. The proposed RoI-based CNN feature describes the patterns of the detected RoIs,so that the visual matching can be implemented at image region-level to effectively identifytarget objects from cluttered backgrounds. Moreover, we test the performance of theproposed RoI-based CNN feature, when it is extracted from different convolutional layersor fully-connected layers. Also, we compare the performance of RoI-based CNN featurewith those of the state-of-the-art CNN features on two instance retrieval benchmarks.Experimental results show that the proposed RoI-based CNN feature provides superiorperformance than the state-of-the-art CNN features for in-stance retrieval.