WO2022241987A1 - Procédé et appareil de récupération d'image - Google Patents

Procédé et appareil de récupération d'image Download PDF

Info

Publication number
WO2022241987A1
WO2022241987A1 PCT/CN2021/119402 CN2021119402W WO2022241987A1 WO 2022241987 A1 WO2022241987 A1 WO 2022241987A1 CN 2021119402 W CN2021119402 W CN 2021119402W WO 2022241987 A1 WO2022241987 A1 WO 2022241987A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
retrieval
historical
feature
copy
Prior art date
Application number
PCT/CN2021/119402
Other languages
English (en)
Chinese (zh)
Inventor
曾锐
林汉权
林杰兴
Original Assignee
稿定（厦门）科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 稿定（厦门）科技有限公司 filed Critical 稿定（厦门）科技有限公司
Publication of WO2022241987A1 publication Critical patent/WO2022241987A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/53—Querying
    • G06F16/538—Presentation of query results
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G06F16/5846—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using extracted text

Definitions

  • the present invention relates to the technical field of image retrieval, in particular to an image retrieval method, a computer-readable storage medium, a computer device and an image retrieval device.
  • Search by image is a function of image retrieval based on the specified image provided by the user to obtain the target image; this function does not require the user to organize keywords and analyze the retrieval method; it can effectively improve the user's retrieval efficiency and reduce the user's time spent on retrieval. The time spent in the target image process.
  • the entire image is mostly input into the model to extract the features of the entire image; then, the target image is retrieved based on the features of the entire image.
  • This method tends to ignore the important information of the specified image, resulting in inaccurate retrieval results of the final target image.
  • an object of the present invention is to propose an image retrieval method, which can extract feature information of images from multiple dimensions, deeply mine potential information of original images, and further improve the accuracy of image retrieval.
  • a second object of the present invention is to propose a computer-readable storage medium.
  • a third object of the present invention is to propose a computer device.
  • the fourth object of the present invention is to provide an image retrieval device.
  • the embodiment of the first aspect of the present invention proposes an image retrieval method, including the following steps: acquiring historical images, and performing saliency detection on the historical images through a pre-trained saliency detection network, and according to The significance detection result performs semantic extraction on the historical image to obtain the semantic features of the historical image; performs text extraction on the historical image, and calculates the text feature corresponding to the historical image according to the text extraction result;
  • the historical image is input to the style recognition model to obtain the style feature of the historical image;
  • the retrieval vector corresponding to the historical image is calculated according to the semantic feature, the copy feature and the style feature, and according to a plurality of the historical
  • the image and the retrieval vector corresponding to each historical image generate a retrieval database; obtain the image to be retrieved, and calculate the retrieval vector corresponding to the image to be retrieved, and calculate the retrieval database according to the retrieval vector and the retrieval vector
  • the image retrieval method of the embodiment of the present invention first, obtain historical images, and perform saliency detection on the historical images through a pre-trained saliency detection network, so as to extract the main part in the historical images; then, according to the saliency detection results Semantic analysis is performed on historical images to obtain the semantic features of historical images; then, copywriting is extracted from historical images, and the corresponding copywriting features of historical images are calculated according to the results of copywriting extraction; then, historical images are input into the style recognition model to The style features of historical images are extracted through the style recognition model; then, the semantic features, copy features and style features are fused to obtain a retrieval vector; and the historical images and corresponding retrieval vectors are added to the retrieval database to pass multiple The historical images and their corresponding retrieval vectors are used to generate a retrieval database; then, the images to be retrieved are obtained, and the retrieval vectors corresponding to the images to be retrieved are calculated, and according to the retrieval vectors corresponding to the retrieval vectors and any one of the
  • the similarity values between the historical images then, according to the similarity values corresponding to all historical images, return the retrieval results corresponding to the images to be retrieved; thereby extracting the feature information of the image from multiple dimensions, deeply mining the potential information of the original image, and then improving Accuracy of Image Retrieval.
  • image retrieval method proposed according to the above-mentioned embodiments of the present invention may also have the following additional technical features:
  • the training of the saliency detection network includes: acquiring an open-source dataset and a subject-free image, extracting subject information of images in the open-source dataset, and fusing the subject information with the subject-free image; A training set is generated according to the fusion result of the open source data set and the subject information and the subject-free image, so as to train the saliency detection network according to the training set.
  • calculating the copy feature corresponding to the historical image according to the copy extraction result includes: performing word segmentation and keyword extraction on the copy extraction result to generate keywords corresponding to the copy extraction result and weights corresponding to the keyword;
  • the keywords are mapped to keyword vectors, and a weighted average is performed according to the keyword vectors and corresponding weights to obtain copy features corresponding to the historical images.
  • calculating the retrieval vector corresponding to the historical image according to the semantic feature, the copy feature and the style feature includes: obtaining the weight corresponding to the semantic feature, the weight corresponding to the copy feature, and the The weight corresponding to the style feature, and performing feature fusion on the semantic feature, the copy feature and the style feature according to the weight corresponding to the semantic feature, the weight corresponding to the copy feature, and the weight corresponding to the style feature, to get the retrieval vector.
  • the method further includes: obtaining click data of the user on the search result, and updating the weight corresponding to the semantic feature, the weight corresponding to the copywriting feature, and the weight corresponding to the style feature according to the click data .
  • the embodiment of the second aspect of the present invention provides a computer-readable storage medium on which an image retrieval program is stored, and when the image retrieval program is executed by a processor, the above image retrieval method is realized.
  • the computer-readable storage medium of the embodiment of the present invention by storing the image retrieval program, so that when the processor executes the image retrieval program, the above-mentioned image retrieval method is realized, thereby realizing the feature information extraction of the image from multiple dimensions, Deeply mine the potential information of the original image, and then improve the accuracy of image retrieval.
  • the embodiment of the third aspect of the present invention proposes a computer device, including a memory, a processor, and a computer program stored in the memory and operable on the processor.
  • the processor executes the program, Implement the image retrieval method as described above.
  • the image retrieval program is stored through the memory, so that when the processor executes the image retrieval program, the above-mentioned image retrieval method is realized, thereby realizing the feature information extraction of the image from multiple dimensions, Deeply mine the potential information of the original image, and then improve the accuracy of image retrieval.
  • the embodiment of the fourth aspect of the present invention proposes an image retrieval device, including: a semantic feature module, the semantic feature module is used to obtain historical images, and the pre-trained saliency detection network detects the Performing saliency detection on historical images, and performing semantic extraction on the historical images according to the saliency detection results to obtain semantic features of the historical images; a copy feature module, the copy feature module is used to copy the historical images Extracting, and calculating the copywriting feature corresponding to the historical image according to the copywriting extraction result; a style feature module, the style feature module is used to input the historical image into a style recognition model to obtain the style feature of the historical image; database module, the database module is used to calculate the retrieval vector corresponding to the historical image according to the semantic feature, the copy feature and the style feature, and according to the multiple historical images and the retrieval vector corresponding to each historical image Generate a retrieval database; a retrieval module, the retrieval module is used to obtain images to be retrieved, and calculate
  • the semantic feature module is used to obtain historical images
  • the pre-trained saliency detection network is used to detect the saliency of the historical images
  • the semantics of the historical images is performed according to the saliency detection results.
  • the copy feature module is used to extract the copy of the historical image, and calculate the copy feature corresponding to the historical image according to the copy extraction result;
  • the style feature module is used to input the historical image to the style recognition model to Get the style features of historical images;
  • the database module is used to calculate the retrieval vectors corresponding to the historical images according to the semantic features, copy features and style features, and generate a retrieval database according to multiple historical images and the retrieval vectors corresponding to each historical image;
  • the retrieval module uses To obtain the image to be retrieved, calculate the vector to be retrieved corresponding to the image to be retrieved, and calculate the similarity value between any historical image in the retrieval database and the image to be retrieved according to the vector to be retrieved and the retrieval vector;
  • the feedback module is used to The similarity value corresponding to the image returns the retrieval result corresponding to the image to be retrieved; thus, the feature information of the image can be extracted from multiple dimensions, and the potential information of the original image can be deeply mined, thereby improving the accuracy
  • image retrieval device proposed according to the above-mentioned embodiments of the present invention may also have the following additional technical features:
  • the training of the saliency detection network includes: acquiring an open-source dataset and a subject-free image, extracting subject information of images in the open-source dataset, and fusing the subject information with the subject-free image; A training set is generated according to the fusion result of the open source data set and the subject information and the subject-free image, so as to train the saliency detection network according to the training set.
  • calculating the copy feature corresponding to the historical image according to the copy extraction result includes: performing word segmentation and keyword extraction on the copy extraction result to generate keywords corresponding to the copy extraction result and weights corresponding to the keyword;
  • the keywords are mapped to keyword vectors, and a weighted average is performed according to the keyword vectors and corresponding weights to obtain copy features corresponding to the historical images.
  • Fig. 1 is a schematic flow chart of an image retrieval method according to an embodiment of the present invention
  • Fig. 2 is a schematic block diagram of an image retrieval device according to an embodiment of the present invention.
  • the entire image is mostly input into the model to extract the features of the entire image; then, the target image is retrieved based on the features of the entire image.
  • the image retrieval method of the embodiment of the present invention firstly, the historical image is obtained, and the historical image is processed through the pre-trained saliency detection network.
  • Saliency detection to extract the main part of the historical image; then, carry out semantic analysis on the historical image according to the saliency detection result to obtain the semantic features of the historical image; then, perform copy extraction on the historical image, and calculate according to the copy extraction result
  • the copy features corresponding to the historical image then, input the historical image into the style recognition model to extract the style features of the historical image through the style recognition model; then, perform feature fusion on the semantic feature, copy feature and style feature to obtain the retrieval vector; and adding the historical image and the corresponding retrieval vector to the retrieval database, so as to generate the retrieval database through multiple historical images and their corresponding retrieval vectors; then, obtain the image to be retrieved, and calculate the retrieval vector corresponding to the image to be retrieved, And calculate the similarity value between the image to be retrieved and the historical image according to the retrieval vector corresponding to the vector to be retrieved and any historical image; then, return the retrieval result corresponding to the image to be retrieved according to the similarity values corresponding to all historical images; thereby real
  • Fig. 1 is a schematic flow chart of an image retrieval method according to an embodiment of the present invention. As shown in Fig. 1, the image retrieval method includes the following steps:
  • the training of the saliency detection network includes: obtaining an open source dataset and subject-free images, extracting subject information of images in the open source dataset, and fusing subject information with subject-free images; according to the open source dataset and subject The fusion result of the information and the subject-free image generates a training set, so that the training of the saliency detection network can be performed according to the training set.
  • the training set is generated by manual marking, it will consume a lot of manpower and material resources; therefore, when training the saliency detection network; first, by extracting the subject information corresponding to the image in the open source dataset, and using the The information is fused with the non-subject image to generate a new image; in this way, a large number of training samples can be obtained without manual labeling; the resources required for the training process of the saliency detection network are reduced.
  • calculating the copy feature corresponding to the historical image according to the copy extraction result includes: performing word segmentation and keyword extraction on the copy extraction result to generate keywords corresponding to the copy extraction result and weights corresponding to the keyword; It is mapped to a keyword vector, and a weighted average is performed according to the keyword vector and the corresponding weight to obtain the copy features corresponding to the historical image.
  • crawlers and other technologies are used to search the public copywriting on the Internet, so as to generate a training data set according to the collected data; then, the word2vector model and word segmentation model are trained according to the training data set; then, the history The text detection and recognition of the image is used to extract the text part in the historical image; then, the text part is segmented and the keywords are extracted through the word segmentation model to obtain the corresponding keywords and the corresponding weight of each keyword; then, through word2vector Each keyword is mapped to a corresponding keyword vector; then, weighted summation is performed according to the keyword vector and weight corresponding to the keyword to obtain the copy feature vector corresponding to the historical image.
  • the style recognition of historical images is carried out through the pre-trained style recognition model (it is understandable that each image will have its corresponding style; for example, most of the Spring Festival posters will use red as the main color to highlight the festive atmosphere); to obtain the style features of historical images; it can be understood that this style recognition will effectively improve the accuracy of subsequent image retrieval.
  • the training of the style recognition model may include: first, obtaining the result image corresponding to the image template (that is, the image generated by the image template), so as to use the result image corresponding to the same image template as an image of the same style; In this way, a large amount of effective training data can be obtained. Further, the dominant color of each result image in the same style can be extracted, and the color distance of the dominant color between the result images can be calculated to filter out the result images that obviously do not belong to the same style, and determine the final training data.
  • ResNet50 combined with triplet loss can be used to train a style recognition model.
  • S104 Calculate retrieval vectors corresponding to the historical images according to the semantic features, copywriting features and style features, and generate a retrieval database according to multiple historical images and the retrieval vectors corresponding to each historical image.
  • the calculation of the retrieval vector corresponding to the historical image is performed according to the semantic features, copy features and style features; furthermore, after the calculation is completed, the historical image and the corresponding retrieval vector are added to the retrieval database; thus, based on multiple historical images
  • the retrieval vector corresponding to each historical image can construct a retrieval database, so that subsequent image retrieval can be performed according to the retrieval database.
  • calculating the retrieval vector corresponding to the historical image according to the semantic feature, the copy feature and the style feature includes: obtaining the weight corresponding to the semantic feature, the weight corresponding to the copy feature and the weight corresponding to the style feature, and according to the corresponding weight of the semantic feature Weights, weights corresponding to copywriting features, and weights corresponding to style features perform feature fusion on semantic features, copywriting features, and style features to obtain retrieval vectors.
  • semantic features, copywriting features, and style features are all one-dimensional vectors with a length of 128, which are verctor1, vecotr2, and vector3; then, define the weights corresponding to the three features as a1, a2, and a3; then finally The retrieval vector of is expressed as: a1*vector1+a2*vector2+a3*vector3.
  • the image retrieval method proposed by the embodiment of the present invention further includes: acquiring the user's click data on the retrieval results, and performing the weight corresponding to the semantic feature, the weight corresponding to the copy feature, and the weight corresponding to the style feature according to the click data. renew.
  • the initial weight (for example, 1, 1, 1) may be used for calculation in combination with the values of the three features.
  • the accuracy of the search results can be judged by obtaining the user's click data on the search results; furthermore, according to the click data, the weights corresponding to the semantic features, the weights corresponding to the copy features, and the weights corresponding to the style features Updating can effectively improve the accuracy of the final weight setting; thereby improving the accuracy of the final image retrieval.
  • S105 Acquire an image to be retrieved, calculate a vector to be retrieved corresponding to the image to be retrieved, and calculate a similarity value between any historical image in the retrieval database and the image to be retrieved according to the vector to be retrieved and the retrieval vector.
  • the image to be retrieved uploaded by the user obtains the image to be retrieved uploaded by the user, extract the semantic feature, copy feature and style feature corresponding to the image to be retrieved, and fuse the three features to obtain the vector to be retrieved corresponding to the image to be retrieved; then, calculate The cosine similarity between the vector to be retrieved and the retrieval image corresponding to any historical image in the retrieval database; the cosine similarity is used as the similarity value between the image to be retrieved and the historical image; thus, the traversal retrieval
  • the database can calculate the similarity value between the image to be retrieved and each historical image; then, sort the historical images according to the size of the similarity value, and return the retrieval result corresponding to the image to be retrieved according to the sorting result.
  • the image retrieval method of the embodiment of the present invention first, obtain historical images, and perform saliency detection on the historical images through a pre-trained saliency detection network, so as to extract the main part in the historical images; then, According to the saliency detection results, the historical images are semantically analyzed to obtain the semantic features of the historical images; then, the historical images are extracted from the text, and the corresponding copy features of the historical images are calculated according to the text extraction results; then, the historical images are input into the style In the recognition model, the style feature of the historical image is extracted through the style recognition model; then, the semantic feature, copy feature and style feature are fused to obtain a retrieval vector; and the historical image and the corresponding retrieval vector are added to the retrieval database , to generate a retrieval database through multiple historical images and their corresponding retrieval vectors; then, obtain the image to be retrieved, and calculate the retrieval vector corresponding to the retrieval image, and calculate according to the retrieval vector corresponding to the retrieval vector and any historical image
  • an embodiment of the present invention proposes a computer-readable storage medium on which an image retrieval program is stored, and when the image retrieval program is executed by a processor, the above-mentioned image retrieval method is implemented.
  • the computer-readable storage medium of the embodiment of the present invention by storing the image retrieval program, so that when the processor executes the image retrieval program, the above-mentioned image retrieval method is realized, thereby realizing the feature information extraction of the image from multiple dimensions, Deeply mine the potential information of the original image, and then improve the accuracy of image retrieval.
  • the embodiment of the present invention proposes a computer device, including a memory, a processor, and a computer program stored on the memory and operable on the processor.
  • the processor executes the program, the following The image retrieval method described above.
  • the image retrieval program is stored through the memory, so that when the processor executes the image retrieval program, the above-mentioned image retrieval method is realized, thereby realizing the feature information extraction of the image from multiple dimensions, Deeply mine the potential information of the original image, and then improve the accuracy of image retrieval.
  • the embodiment of the present invention proposes an image retrieval device, as shown in FIG. module 50 and feedback module 60 .
  • the semantic feature module 10 is used to obtain historical images, and perform saliency detection on historical images through a pre-trained saliency detection network, and perform semantic extraction on historical images according to the saliency detection results to obtain semantic features of historical images ;
  • the copy feature module 20 is used to extract the text of the historical image, and calculate the corresponding text feature of the historical image according to the text extraction result;
  • the style feature module 30 is used for inputting the historical image into the style recognition model, to obtain the style feature of the historical image;
  • the database module 40 is used to calculate the retrieval vectors corresponding to the historical images according to the semantic features, copywriting features and style features, and generate a retrieval database according to multiple historical images and the retrieval vectors corresponding to each historical image;
  • the retrieval module 50 is used to obtain the image to be retrieved, and calculate the vector to be retrieved corresponding to the image to be retrieved, and calculate the similarity value between any historical image in the retrieval database and the image to be retrieved according to the vector to be retrieved and the retrieval vector;
  • the feedback module 60 is used to return the retrieval result corresponding to the image to be retrieved according to the similarity values corresponding to all historical images.
  • the training of the saliency detection network includes: obtaining an open source dataset and subject-free images, extracting subject information of images in the open source dataset, and fusing subject information with subject-free images; according to the open source dataset and subject The fusion result of the information and the subject-free image generates a training set, so that the training of the saliency detection network can be performed according to the training set.
  • calculating the copy feature corresponding to the historical image according to the copy extraction result includes: performing word segmentation and keyword extraction on the copy extraction result to generate keywords corresponding to the copy extraction result and weights corresponding to the keyword; It is mapped to a keyword vector, and a weighted average is performed according to the keyword vector and the corresponding weight to obtain the copy features corresponding to the historical image.
  • the image retrieval device of the embodiment of the present invention by setting the semantic feature module to obtain historical images, and performing saliency detection on historical images through a pre-trained saliency detection network, and according to the saliency detection results Semantic extraction of historical images to obtain the semantic features of historical images; the copy feature module is used to extract text from historical images, and calculates the corresponding copy features of historical images according to the results of text extraction; the style feature module is used to input historical images into The style recognition model is used to obtain the style features of historical images; the database module is used to calculate the retrieval vectors corresponding to historical images based on semantic features, copy features and style features, and generate retrievals based on multiple historical images and the retrieval vectors corresponding to each historical image database; the retrieval module is used to obtain the image to be retrieved, and calculate the vector to be retrieved corresponding to the image to be retrieved, and calculate the similarity value between any historical image in the retrieval database and the image to be retrieved according to the vector to be retrieved and the retrieval
  • the embodiments of the present invention may be provided as methods, systems, or computer program products. Accordingly, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions
  • the device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.
  • any reference signs placed between parentheses shall not be construed as limiting the claim.
  • the word “comprising” does not exclude the presence of elements or steps not listed in a claim.
  • the word “a” or “an” preceding an element does not exclude the presence of a plurality of such elements.
  • the invention can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means can be embodied by one and the same item of hardware.
  • the use of the words first, second, and third, etc. does not indicate any order. These words can be interpreted as names.
  • first and second are used for description purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Thus, a feature defined as “first” and “second” may explicitly or implicitly include one or more of these features.
  • “plurality” means two or more, unless otherwise specifically defined.
  • the first feature may be in direct contact with the first feature or the first and second feature may be in direct contact with the second feature through an intermediary. touch.
  • “above”, “above” and “above” the first feature on the second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature.
  • “Below”, “beneath” and “beneath” the first feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is less horizontally than the second feature.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Library & Information Science (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Image Analysis (AREA)

Abstract

La présente divulgation divulgue un procédé et un appareil de récupération d'image, ainsi qu'un support et un dispositif. Le procédé de récupération d'image comprend les étapes consistant à : acquérir des images historiques, effectuer une détection de relief sur les images historiques, et effectuer une extraction sémantique sur les images historiques selon des résultats de détection de relief, de façon à obtenir des caractéristiques sémantiques des images historiques ; calculer des caractéristiques de texte correspondant aux images historiques ; entrer les images historiques dans un modèle de reconnaissance de style, de façon à obtenir des caractéristiques de style des images historiques ; en fonction des caractéristiques sémantiques, des caractéristiques de texte et des caractéristiques de style, calculer des vecteurs de récupération correspondant aux images historiques, et générer une base de données de récupération ; acquérir une image à soumettre à une récupération, calculer un vecteur à soumettre à une récupération correspondant à ladite image, et en fonction dudit vecteur et d'un vecteur de récupération, calculer une valeur de similarité entre n'importe quelle image historique dans la base de données de récupération et ladite image ; et en fonction des valeurs de similarité correspondant à toutes les images historiques, renvoyer un résultat de récupération correspondant à ladite image. Des informations de caractéristiques d'images peuvent être extraites d'une pluralité de dimensions, et des informations potentielles d'une image d'origine peuvent être extraites en profondeur, ce qui permet d'améliorer la précision de récupération d'image.
PCT/CN2021/119402 2021-05-18 2021-09-18 Procédé et appareil de récupération d'image WO2022241987A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110539585.4 2021-05-18
CN202110539585.4A CN113282781B (zh) 2021-05-18 2021-05-18 图像检索方法及装置

Publications (1)

Publication Number Publication Date
WO2022241987A1 true WO2022241987A1 (fr) 2022-11-24

Family

ID=77279558

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/119402 WO2022241987A1 (fr) 2021-05-18 2021-09-18 Procédé et appareil de récupération d'image

Country Status (2)

Country Link
CN (1) CN113282781B (fr)
WO (1) WO2022241987A1 (fr)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113282781B (zh) * 2021-05-18 2022-06-28 稿定（厦门）科技有限公司 图像检索方法及装置

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107291855A (zh) * 2017-06-09 2017-10-24 中国电子科技集团公司第五十四研究所 一种基于显著对象的图像检索方法及系统
CN108829826A (zh) * 2018-06-14 2018-11-16 清华大学深圳研究生院 一种基于深度学习和语义分割的图像检索方法
CN110598037A (zh) * 2019-09-23 2019-12-20 腾讯科技（深圳）有限公司 一种图像搜索方法、装置和存储介质
CN110866140A (zh) * 2019-11-26 2020-03-06 腾讯科技（深圳）有限公司 图像特征提取模型训练方法、图像搜索方法及计算机设备
US20200175062A1 (en) * 2017-07-28 2020-06-04 Hangzhou Hikvision Digital Technology Co., Ltd. Image retrieval method and apparatus, and electronic device
CN113282781A (zh) * 2021-05-18 2021-08-20 稿定（厦门）科技有限公司 图像检索方法及装置

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11144587B2 (en) * 2016-03-08 2021-10-12 Shutterstock, Inc. User drawing based image search
CN106649487B (zh) * 2016-10-09 2020-02-18 苏州大学 基于兴趣目标的图像检索方法
CN111415396A (zh) * 2019-01-08 2020-07-14 腾讯科技（深圳）有限公司 一种图像生成方法、装置和存储介质
CN110297931B (zh) * 2019-04-23 2021-12-03 西北大学 一种图像检索方法
CN110175249A (zh) * 2019-05-31 2019-08-27 中科软科技股份有限公司 一种相似图片的检索方法及系统
CN110288019A (zh) * 2019-06-21 2019-09-27 北京百度网讯科技有限公司 图像标注方法、装置和存储介质

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107291855A (zh) * 2017-06-09 2017-10-24 中国电子科技集团公司第五十四研究所 一种基于显著对象的图像检索方法及系统
US20200175062A1 (en) * 2017-07-28 2020-06-04 Hangzhou Hikvision Digital Technology Co., Ltd. Image retrieval method and apparatus, and electronic device
CN108829826A (zh) * 2018-06-14 2018-11-16 清华大学深圳研究生院 一种基于深度学习和语义分割的图像检索方法
CN110598037A (zh) * 2019-09-23 2019-12-20 腾讯科技（深圳）有限公司 一种图像搜索方法、装置和存储介质
CN110866140A (zh) * 2019-11-26 2020-03-06 腾讯科技（深圳）有限公司 图像特征提取模型训练方法、图像搜索方法及计算机设备
CN113282781A (zh) * 2021-05-18 2021-08-20 稿定（厦门）科技有限公司 图像检索方法及装置

Also Published As

Publication number Publication date
CN113282781B (zh) 2022-06-28
CN113282781A (zh) 2021-08-20

Similar Documents

Publication Publication Date Title
US9430719B2 (en) System and method for providing objectified image renderings using recognition information from images
US7809192B2 (en) System and method for recognizing objects from images and identifying relevancy amongst images and information
Torralba et al. Labelme: Online image annotation and applications
US8649572B2 (en) System and method for enabling the use of captured images through recognition
WO2019169872A1 (fr) Procédé et dispositif de recherche de ressource de contenu, et serveur
US10963504B2 (en) Zero-shot event detection using semantic embedding
CN102549603B (zh) 基于相关性的图像选择
CN108334627B (zh) 新媒体内容的搜索方法、装置和计算机设备
Li et al. Bootstrapping visual categorization with relevant negatives
August et al. AI naturalists might hold the key to unlocking biodiversity data in social media imagery
WO2022134701A1 (fr) Procédé et appareil de traitement vidéo
CN103988202A (zh) 基于索引和搜索的图像吸引力
Weyand et al. Visual landmark recognition from internet photo collections: A large-scale evaluation
CN109271624B (zh) 一种目标词确定方法、装置及存储介质
WO2022241987A1 (fr) Procédé et appareil de récupération d'image
Choi et al. Multimodal location estimation of consumer media: Dealing with sparse training data
Patwardhan et al. ViTag: Automatic video tagging using segmentation and conceptual inference
JPH11250106A (ja) 内容基盤の映像情報を利用した登録商標の自動検索方法
Panda et al. Heritage app: annotating images on mobile phones
CN111752922A (zh) 一种建立知识数据库、实现知识查询的方法及装置
Averbuch‐Elor et al. Distilled collections from textual image queries
Demidova et al. Semantic image-based profiling of users’ interests with neural networks
Lee et al. A scalable service for photo annotation, sharing, and search
CN117830601B (zh) 一种基于弱监督的三维视觉定位方法、装置、设备及介质
Badghaiya et al. Image classification using tag and segmentation based retrieval

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21940435

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21940435

Country of ref document: EP

Kind code of ref document: A1