Research On Retrieval Method Of Civil Aviation Lost Baggage Based On Hashing Method

Luggage images and passenger identity information would be saved when air passengers use automated and intelligent baggage check-in equipment.The luggage images and passenger identity information can be used as the basis for retrieving luggage in the event of loss.Lost air baggage recovery promotion is an important job of civil aviation service satisfaction.Abstracted as large-scale image retrieval task in the field of computer vision,the core of baggage image retrieval is to find suitable image expression and similarity measure criterion.Aiming at the application of hash retrieval in the aviation field,the retrieval methods of civil aviation passenger baggage were studied.The retrieval method based on the deep hash model was proposed to improve the success rate of passenger baggage recovery after loss.A baggage image segmentation algorithm based on SEEDS and graph cut algorithm is proposed.Background noise in aviation baggage retrieval tasks is contained in images.The retrieval effect of lost checked baggage might be influenced by noise such as image background.To improve retrieval precision,a bilateral filtering algorithm is proposed to improve the segmentation effect of SEEDS.The improved baggage image segmentation algorithm can eliminate local details impact,at the mean time reduce the excessive segmentation and improve the regularity of superpixel.Combined with Graph Cut algorithm based on energy optimization,the superpixels are classified and segmented into baggage area and background area.Experiments show that the proposed method is effective for air baggage image segmentation.A hash function based on deep learning was designed.Searching luggage by graph should minimize time consumption on the basis of satisfying retrieval accuracy requirements.Referring to the application of hash method in image similarity retrieval task,the setting methods of three hash functions are analyzed.Hash function based on depth model is designed,combining the characteristics of baggage image retrieval task.Image similarity feature learned by deep convolutional neural network and quantized into binary hash code.Retrieval speed was improved by hashing while preserving image similarity.The application of relevance feedback technology in image retrieval tasks was researched.Integrating human understanding of retrieval targets into retrieval systems helps to find target luggage images more accurately.Feedback information is added to the air baggage image retrieval.Modify the retrieval parameters by feedback the user’s subjective opinion on the retrieval results using feature coding to the system.Adding feedback information,more consistent retrieval results got by the retrieval system with users’ subjective opinions.