The invention discloses an automatic image annotation method based on semi-supervised learning. The method comprises the steps that a data set is divided into a training data set, an unlabeled data set and a test set; the SIFT feature and the HOG feature of a training sample are extracted to train an LDA_SVM classifier; color and texture features are extracted to train a neural network; unlabeleddata are used to enable two classifiers to label and predict the same unlabeled sample simultaneously; according to the contribution of the classifiers to the unlabeled sample classification accuracy,the classification results of two classifiers are weighted and fused by an adaptive weighted fusion policy, so as to acquire the final prediction label probability vector of the sample; and finally two classifiers are updated by the sample with high confidence and the predictive label thereof until the preset maximum number of iterations is reached. According to the invention, the method can makefull use of the unlabeled sample to excavate the inherent law of the image feature; the number of annotation samples required for the classifier training is effectively reduced; the annotation effectis great.

Automatic image annotation method based on semi-supervised learning

The present application discloses a method for assessing aesthetic quality of a natural image based on multi-task deep learning. Said method includes: step 1: automatically learning aesthetic and semantic characteristics of the natural image based on multi-task deep learning; step 2: performing aesthetic categorization and semantic recognition to the results of automatic learning based on multi-task deep learning, thereby realizing assessment of aesthetic quality of the natural image. The present application uses semantic information to assist learning of expressions of aesthetic characteristics so as to assess aesthetic quality more effectively, besides, the present application designs various multi-task deep learning network structures so as to effectively use the aesthetic and semantic information for obtaining highly accurate image aesthetic categorization. The present application can be applied to many fields relating to image aesthetic quality assessment, including image retrieval, photography and album management, etc.

Method for assessing aesthetic quality of natural image based on multi-task deep learning

Disclosed is a method for generating a semantic image labeling model, comprising: forming a first CNN and a second CNN, respectively; randomly initializing the first CNN; inputting a raw image and a plurality of predetermined label ground truth annotations to the first CNN to iteratively update weights of the first CNN so that a category label probability for the raw image, which is output from the first CNN, approaches the predetermined label ground truth annotations; randomly initializing the second CNN; inputting the category label probability to the second CNN to correct the input category label probability so as to determine classification errors of the category label probabilities; updating the second CNN by back-propagating the classification errors; concatenating the updated first CNN and the updated second CNN; classifying each pixel in the raw image into one of a plurality of general object categories; and back-propagating classification errors through the concatenated CNN to update weights of the concatenated CNN until the classification errors less than a predetermined threshold.

An apparatus and a method for semantic image labeling

A method for multi-task deep learning-based aesthetic quality assessment on a natural image. The method comprises: step 1, automatically learning about multi-task deep learning-based aesthetic and semantic features of a natural image (S101); and step 2, performing multi-task deep learning-based aesthetic classification and semantic identification on the automatic learning result to implement aesthetic quality assessment on the natural image (S102). The method more efficiently performs aesthetic quality assessment by assisting the expression and learning of aesthetic features using semantic information, and designs a plurality of multi-task deep learning network structures to more efficiently obtain a high-precision classification of an image using aesthetic and semantic information. The method can be applied to many fields related to aesthetic quality assessment of images, including image searching, photography, album management, and the like.

Method for multi-task deep learning-based aesthetic quality assessment on natural image

The invention provides a Bayesian probability estimation model, aiming at the problem of a semantic gap in image search at present. Probability latent semantic subject term (hereinafter referred to as a subject term) parameters are added on the basis of image semantic retrieval to establish a document-subject term-semantic characteristic word probability relation; a Bayesian probability estimation algorithm theory is combined to calculate posterior probability, wherein the posterior probability is equal to an arithmetic product of prior probability and a likelihood function; a maximum likelihood estimated value of the posterior probability is solved by using an EM algorithm, so that the Bayesian probability estimation model is established. In view of the document-subject term-semantic characteristic word relation, a document is mapped, and the document corresponds to an image, so that the image needed by a user is finally displayed.

Probability latent parameter estimation model of image semantic data based on Bayesian algorithm

The invention discloses a method for semantically annotating images on the basis of hybrid generative and discriminative learning models. The method includes generatively building models of the images by means of continuous PLSA (probabilistic latent semantic analysis) at generative learning stages, acquiring corresponding model parameters and subject distribution of each image, and utilizing the corresponding subject distribution as an intermediate representation vector of each image; constructing cluster classifier chains to discriminatively learn from the intermediate representation vectors of the images at discriminative learning stages, creating the classifier chains and integrating contextual information among annotation keywords; automatically extracting visual features of each given unknown image at annotation stages, acquiring representation of subject vectors of the given unknown images by the aid of a continuous PLSA parameter estimation algorithm, classifying the subject vectors by the aid of trained cluster classifier chains and semantically annotating the images by a plurality of semantic keywords with the highest confidence. The method has the advantage that the annotation and retrieval performance of the method are superior to the annotation and retrieval performance of most current typical methods for automatically annotating images.

Method for semantically annotating images on basis of hybrid generative and discriminative learning models

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Li Zhixin

Author Tools

Chat about Author

Papers

Method for semantically annotating images on basis of hybrid generative and discriminative learning models