Deep learning-based freehand sketch image retrieval method

An image retrieval and deep learning technology, applied in the field of hand-drawn sketch image retrieval based on deep learning, can solve the problems of high ranking position, affecting user experience, ignoring the semantic information of sketches, etc., to achieve the elimination of ambiguity, high accuracy and adaptability strong effect

Active Publication Date: 2016-11-16
FUDAN UNIV
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Problems solved by technology

[0019] (2) Fully mine the multi-level information of sketches - most existing sketch retrieval techniques only consider the visual information of sketches, ignoring the high-level semantic information of sketches
However, in most retrievals, the

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  • Deep learning-based freehand sketch image retrieval method
  • Deep learning-based freehand sketch image retrieval method
  • Deep learning-based freehand sketch image retrieval method

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Embodiment Construction

[0086] The specific implementation details of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0087] (1) Image acquisition and preprocessing

[0088] The color pictures of the multimedia data set are collected as an image database, and all images are unified in JPG format. Then adjust the size of each picture to 256*256. Since the present invention only considers that each picture has a single-category labeling information, and uses the image data of the category label to retrain the CNN model, the image categories in the database are limited. For other categories of images, directly remove or retain a small amount of noise images, and remove redundant images.

[0089] (2) Generation of sketch-like images

[0090] figure 2 Shows the process of converting a color image to a class sketch using the two-step transformation method mentioned earlier. In the present invention, SE edge detection algorithm is firstly used t...

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Abstract

The invention belongs to the technical field of multimedia information retrieval, and specifically discloses a deep learning-based freehand sketch image retrieval method. According to the method, and edge contour detection technology and a non-maximum value suppression technology are utilized to realize the conversion from colored images to similar sketch images, a deep learning technology is utilized to construct distinguishing feature expressions for querying deep features of sketches and similar sketches, the deep features fuse the high-level semantic features and low-level visual features of images, and the deep features are more distinguishing in sketch retrieval. Through deeply mining visual information of a first retrieval result, uncorrelated images placed at the front in the retrieval result are rejected and a more correlated result is returned to the users. The method is high in correctness and strong in adaptability. On the basis of large-scale image data, the method is significant in carrying out efficient image retrieval which considers semantic information of sketches, so that the influences of fuzziness of freehand sketches can be decreased, the retrieval correlation can be improved and the user experience can be enhanced; and the method has an extensive application value in the field of multimedia image retrieval.

Description

technical field [0001] The invention belongs to the technical field of multimedia information retrieval, and in particular relates to a hand-drawn sketch image retrieval method based on deep learning. Background technique [0002] With the popularity of image acquisition devices such as mobile phones and digital cameras and the development of Internet technology, digital images have exploded in the past few decades. Some image sharing websites, such as Flickr, upload millions of images every day. How to effectively search for images has become a hot research object in academia and industry, and many image retrieval systems have emerged as a result. Early image retrieval technologies are mainly divided into two categories according to different input types. The first is Text-based Image Retrieval (TBIR), and the second is Content-based Image Retrieval (Content-based Image Retrieval). , CBIR). [0003] Text-based image retrieval technology refers to the realization of retrie...

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Application Information

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IPC IPC(8): G06F17/30G06K9/62
CPCG06F16/583G06F18/23213
Inventor 张玥杰黄飞金城张涛
Owner FUDAN UNIV
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