Image Retrieval with Similar Object Detection and Local Similarity to Detected Objects

2019 ◽  
pp. 42-55
Author(s):  
Sidra Hanif ◽  
Chao Li ◽  
Anis Alazzawe ◽  
Longin Jan Latecki
Keyword(s):  
Image Retrieval ◽  
Object Detection ◽  
Local Similarity ◽  
Similar Object

scholarly journals The Study of Multiple Classes Boosting Classification Method Based on Local Similarity

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 37
Author(s):  
Shixun Wang ◽  
Qiang Chen
Keyword(s):  
Image Retrieval ◽  
Loss Function ◽  
Single Mode ◽  
Local Similarity ◽  
Text And Image ◽  
Data Set ◽  
Standard Data ◽  
Weak Learner ◽  
Great Progress ◽  
Data Points

Boosting of the ensemble learning model has made great progress, but most of the methods are Boosting the single mode. For this reason, based on the simple multiclass enhancement framework that uses local similarity as a weak learner, it is extended to multimodal multiclass enhancement Boosting. First, based on the local similarity as a weak learner, the loss function is used to find the basic loss, and the logarithmic data points are binarized. Then, we find the optimal local similarity and find the corresponding loss. Compared with the basic loss, the smaller one is the best so far. Second, the local similarity of the two points is calculated, and then the loss is calculated by the local similarity of the two points. Finally, the text and image are retrieved from each other, and the correct rate of text and image retrieval is obtained, respectively. The experimental results show that the multimodal multi-class enhancement framework with local similarity as the weak learner is evaluated on the standard data set and compared with other most advanced methods, showing the experience proficiency of this method.

scholarly journals Saliency Object Detection Based on Regions Merging and Its Application in Image Retrieval

2019 ◽  
Vol 1302 ◽  
pp. 022005
Author(s):  
Weiyi Wei ◽  
Hui Chen ◽  
Yahong Wen ◽  
Yu Wang
Keyword(s):  
Image Retrieval ◽  
Object Detection ◽  
Saliency Object Detection

scholarly journals Deep Multiple Instance Hashing for Object-based Image Retrieval

2017 ◽  
Author(s):  
Wanqing Zhao ◽  
Ziyu Guan ◽  
Hangzai Luo ◽  
Jinye Peng ◽  
Jianping Fan
Keyword(s):  
Image Retrieval ◽  
Object Detection ◽  
Multiple Instance Learning ◽  
Feature Maps ◽  
Keyword Query ◽  
The Arts ◽  
Object Based ◽  
Object Proposals ◽  
Weakly Supervised ◽  
Image Pairs

Multi-keyword query is widely supported in text search engines. However, an analogue in image retrieval systems, multi-object query, is rarely studied. Meanwhile, traditional object-based image retrieval methods often involve multiple steps separately and need expensive location labeling for detecting objects. In this work, we propose a weakly-supervised Deep Multiple Instance Hashing (DMIH) framework for object-based image retrieval. DMIH integrates object detection and hashing learning on the basis of a popular CNN model to build the end-to-end relation between a raw image and the binary hashing codes of multiple objects in it. Specifically, we cast the object detection of each object class as a binary multiple instance learning problem where instances are object proposals extracted from multi-scale convolutional feature maps. For hashing training, we sample image pairs to learn their semantic relationships in terms of hash codes of the most probable proposals for owned labels as guided by object predictors. The two objectives benefit each other in learning. DMIH outperforms state-of-the-arts on public benchmarks for object-based image retrieval and achieves promising results for multi-object queries.

scholarly journals SINGLE-SHOT SEMANTIC MATCHER FOR UNSEEN OBJECT DETECTION

2018 ◽  
Vol XLII-2 ◽  
pp. 379-384 ◽  
Author(s):  
V. Gorbatsevich ◽  
Y. Vizilter ◽  
V. Knyaz ◽  
A. Moiseenko
Keyword(s):  
Image Retrieval ◽  
Object Detection ◽  
Image Matching ◽  
Single Shot ◽  
Semantic Matching ◽  
Test Image ◽  
Matching Problem ◽  
Semantic Class ◽  
Bounding Boxes ◽  
Testing Protocols

In this paper we combine the ideas of image matching, object detection, image retrieval and zero-shot learning for stating and solving the semantic matching problem. Semantic matcher takes two images (test and request) as input and returns detected objects (bounding boxes) on test image corresponding to semantic class represented by request (sample) image. We implement our single-shot semantic matcher CNN architecture based on GoogleNet and YOLO/DetectNet architectures. We propose the detection-by-request training and testing protocols for semantic matching algorithms. We train and test our CNN on the ILSVRC 2014 with 200 seen and 90 unseen classes and provide the real-time object detection with mAP 23 for seen and mAP 21 for unseen classes.

Aggregation of Deep Features for Image Retrieval Based on Object Detection

2019 ◽  
pp. 553-564 ◽  
Author(s):  
Juan Ignacio Forcén ◽  
Miguel Pagola ◽  
Edurne Barrenechea ◽  
Humberto Bustince
Keyword(s):  
Image Retrieval ◽  
Object Detection

Color Object Detection Based Image Retrieval Using ROI Segmentation with Multi-Feature Method

2020 ◽  
Vol 112 (1) ◽  
pp. 169-192 ◽  
Author(s):  
Rohit Raja ◽  
Sandeep Kumar ◽  
Md Rashid Mahmood
Keyword(s):  
Image Retrieval ◽  
Object Detection
Sign in / Sign up

Export Citation Format

Share Document