Large Scale Specific Object Recognition by Using GIFTS Image Feature

2015 ◽  
pp. 36-45
Author(s):  
Hiroki Nakano ◽  
Yumi Mori ◽  
Chiaki Morita ◽  
Shingo Nagai
Keyword(s):  
Object Recognition ◽  
Large Scale ◽  
Image Feature ◽  
Specific Object

scholarly journals An Advanced Relevance Feedback Method to Improve Performance of CBIR using Convolutional Neural Network and Comprehensive Values

2019 ◽  
Vol 9 (2) ◽  
pp. 5427-5438
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Activation Function ◽  
Image Feature ◽  
Similarity Measurement ◽  
Query Image ◽  
Image Production

Content-Based Image Retrieval (CBIR) is extensively used technique for image retrieval from large image databases. However, users are not satisfied with the conventional image retrieval techniques. In addition, the advent of web development and transmission networks, the number of images available to users continues to increase. Therefore, a permanent and considerable digital image production in many areas takes place. Quick access to the similar images of a given query image from this extensive collection of images pose great challenges and require proficient techniques. From query by image to retrieval of relevant images, CBIR has key phases such as feature extraction, similarity measurement, and retrieval of relevant images. However, extracting the features of the images is one of the important steps. Recently Convolutional Neural Network (CNN) shows good results in the field of computer vision due to the ability of feature extraction from the images. Alex Net is a classical Deep CNN for image feature extraction. We have modified the Alex Net Architecture with a few changes and proposed a novel framework to improve its ability for feature extraction and for similarity measurement. The proposal approach optimizes Alex Net in the aspect of pooling layer. In particular, average pooling is replaced by max-avg pooling and the non-linear activation function Maxout is used after every Convolution layer for better feature extraction. This paper introduces CNN for features extraction from images in CBIR system and also presents Euclidean distance along with the Comprehensive Values for better results. The proposed framework goes beyond image retrieval, including the large-scale database. The performance of the proposed work is evaluated using precision. The proposed work show better results than existing works.

ObjectNet3D: A Large Scale Database for 3D Object Recognition

2016 ◽  
pp. 160-176 ◽  
Author(s):  
Yu Xiang ◽  
Wonhui Kim ◽  
Wei Chen ◽  
Jingwei Ji ◽  
Christopher Choy ◽  
...  
Keyword(s):  
Object Recognition ◽  
Large Scale ◽  
3D Object Recognition ◽  
3D Object

scholarly journals Large-scale tests of a keyed, appearance-based 3-D object recognition system

1998 ◽  
Vol 38 (15-16) ◽  
pp. 2469-2488 ◽  
Author(s):  
Randal C. Nelson ◽  
Andrea Selinger
Keyword(s):  
Object Recognition ◽  
Large Scale ◽  
Recognition System ◽  
Large Scale Tests

scholarly journals Learning to read does not compete with face and object recognition

2019 ◽  
Author(s):  
Jeroen van Paridon ◽  
Markus Ostarek ◽  
Mrudula Arunkumar ◽  
Falk Huettig
Keyword(s):  
Object Recognition ◽  
Large Scale ◽  
Written Language ◽  
Socioeconomic Background ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Learning To Read ◽  
Test Taking ◽  
Object Categories ◽  
General Object

Human cultural inventions, such as written language, are far too recent for dedicated neural infrastructure to have evolved in its service. Culturally newly acquired skills (e.g. reading) thus ‘recycle’ evolutionarily older circuits that originally evolved for different, but similar functions (e.g. visual object recognition). The destructive competition hypothesis predicts that this neuronal recycling has detrimental effects on the cognitive functions a cortical network originally evolved for. The converse possibility is that learning to read fine-tunes general object recognition mechanisms, resulting in improved recognition across categories. In a large-scale behavioral study with literate, low-literate, and illiterate participants from the same socioeconomic background we find that even after adjusting for cognitive ability and test-taking familiarity, literacy is associated with an increase, rather than a decrease, in object recognition abilities across object categories. These results are incompatible with the claim that neuronal recycling results in destructive competition.

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