Object Recognition under Multifarious Conditions: A Reliability Analysis and A Feature Similarity-based Performance Estimation

Autor: Ghassan AlRegib, Dogancan Temel, Jinsol Lee
Jazyk: angličtina
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
I.2
Similarity (geometry)
I.4
I.5
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
02 engineering and technology
Variation (game tree)
FOS: Electrical engineering
electronic engineering
information engineering
0202 electrical engineering
electronic engineering
information engineering
Feature (machine learning)
Overhead (computing)
Computer vision
Reliability (statistics)
business.industry
Deep learning
Image and Video Processing (eess.IV)
Cognitive neuroscience of visual object recognition
020206 networking & telecommunications
Electrical Engineering and Systems Science - Image and Video Processing
020201 artificial intelligence & image processing
Artificial intelligence
Focus (optics)
business
Zdroj: ICIP
Popis: In this paper, we investigate the reliability of online recognition platforms, Amazon Rekognition and Microsoft Azure, with respect to changes in background, acquisition device, and object orientation. We focus on platforms that are commonly used by the public to better understand their real-world performances. To assess the variation in recognition performance, we perform a controlled experiment by changing the acquisition conditions one at a time. We use three smartphones, one DSLR, and one webcam to capture side views and overhead views of objects in a living room, an office, and photo studio setups. Moreover, we introduce a framework to estimate the recognition performance with respect to backgrounds and orientations. In this framework, we utilize both handcrafted features based on color, texture, and shape characteristics and data-driven features obtained from deep neural networks. Experimental results show that deep learning-based image representations can estimate the recognition performance variation with a Spearman's rank-order correlation of 0.94 under multifarious acquisition conditions.
5 pages, 3 figures, 1 table
Databáze: OpenAIRE
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