Pedestrian and vehicle accessory identification and retrieval method based on deep learning

Abstract

The invention discloses a pedestrian and vehicle accessory identification and retrieval method based on deep learning. The method comprises the following steps that: firstly, sampling eight directionsof a pixel in an image, carrying out quantitative sampling to obtain texture information, and coding two groups of double-cross subsets on each pixel by a double-cross coder to form a total descriptor; according to the descriptor and local gray level simultaneous distribution density , extracting a local histogram vector, and forming texture features; according to the extracted texture features,training to obtain an initial classifier, and setting learning frequencies and accuracy requirements; adopting an active learning algorithm to optimize the classifier, and stopping until a preset accuracy requirement is achieved; and finally, using a multi-instance multi-tag classifier which finishes being trained for identification to obtain a high-accuracy identification result. The system whichis put forward by the invention has the advantages of being high in adaptivity, high in confidence level and steady in integral performance. When image features are extracted, a double-cross mode coding method is adopted, maximum combination entropy can be realized, an image signal-to-noise ratio is maximized, and image robustness is enhanced.

Description

technical field [0001] The invention belongs to the cross technical fields of computer technology, information technology and data mining, and relates to a method and system for identifying and retrieving pedestrian and vehicle accessories based on deep learning. Background technique [0002] Image recognition is a technology for computers to process, analyze and understand images to identify targets and objects in various patterns. The recognition process includes image preprocessing, image segmentation, feature extraction and judgment matching. Retrieval technology refers to the use of computer to learn deeply about images and classify the extracted image features, thereby changing the retrieval technology. Due to the broad application prospects of pedestrian and vehicle accessory recognition, it has gradually become one of the research hotspots in the fields of image understanding, pattern recognition, semantic segmentation, and machine vision. [0003] Active learning i...

AI Technical Summary

Problems solved by technology

However, in many scenarios in real life, it is difficult to obtain labeled samples, which requires experts in the field to manually label, which takes a lot of time and economic costs.

Moreover, if the size of the training samples is too large, the training time will be more, making the learning task extremely difficult, and the performance and reliability of the classifier will be reduced.

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