.png){kind=icon}
返回
Content based image retrieval using image features information fusion
.png){kind=icon}
PRE
AI
DOI：10.1016/j.inffus.2018.11.004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recent image retrieval techniques are focusing on multiple image features for the efficient image retrieval. It has been an inevitable requirement to fetch the images from a variety of semantic groups and datasets. It is vital to retrieve the images based on their primitive features shape, texture, color and spatial information to cater the versatile image datasets. State-of-the-art detectors and descriptors are capable of finding the interest points based on their specialty. To encompass the strength of the image features for the information fusion purpose this contribution presents a novel technique to fuse the spatial color information with shaped extracted features and object recognition. For RGB channels L2 spatial color arrangements are applied and features are extracted, thereby fused with intensity ranged shapes formed by connecting the discovered edges and corners for the grey level image. Perifoveal receptive field estimation with 128-bit cascade matching with symmetric sampling on the detected interest points that discovers the potential information for the complex, overlay, foreground and background objects. Firstly the process is accomplished by reducing the massive features vectors, selecting high variance coefficient and secondly obtaining the indexing and retrieval by employing a Bag-of-Words approach. Extensive experiments are conducted on ten highly recognized image dataset benchmarks, specialized for texture, shapes, colors and objects including ImageNet, Caltech-256, Caltech-101, 102-Flower, Corel-10,000, 17-Flower, Corel-1000, COIL, ALOT and FTVL tropical fruits. To check the affectivity and robustness of the proposed method, it is compared with state-of-the-art detectors and descriptors SIFT, SURF, HOG, LBP, DoG, MSER and RGBLBP. Encouraging results reported that the proposed method has a remarkable performance in most of the image categories of versatile image datasets and can gain better precision to those of the state-of-the-art detectors and descriptors.
Keyword:
Content based image retrieval
Interest point detection
Image descriptor
Features fusion
Bag of words
Spatial color features
Shape features
Objects recognition
Features selection
Information fusion
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
15.5
论文数:
4.2K
被引数:
2.7W
机构
引用论文
.png){kind=icon}
Local neighborhood difference pattern: A new feature descriptor for natural and texture image retrieval局部邻域差异模式: 用于自然和纹理图像检索的新特征描述符
PREAI
分享收藏
Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization用于细粒度图像分类的学习类别特定字典和共享字典
PREAI
分享收藏
查看更多内容