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image retrieval Approach Based on Intuitive Fuzzy Set Combined with Genetic Algorithm

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Journal of Beijing Institute of Technology 2009年第1期18卷 60-64页

作者：王潇茵徐卫华胡昌振 Lab of Computer Network Defense Technology Beijing Institute of Technology Automation Station of Army Aviation Department of the General Staff

Aiming at shortcomings of traditional image retrieval systems, a new image retrieval approach based on color features of image combining intuitive fuzzy theory with genetic algorithm is proposed. Each image is segmented into a constant number of sub-images in vertical direction. Color features are extracted from every sub-image to get chromosome coding. It is considered that fuzzy membership and intuitive fuzzy hesitancy degree of every pixel＇s color in image are associated to all the color histogram bins. Certain feature, fuzzy feature and intuitive fuzzy feature of colors in an image, are used together to describe the content of image. Efficient combinations of sub-image are selected according to operation of selecting, crossing and variation. retrieval results are obtained from image matching based on these color feature combinations of sub-images. Tests show that this approach can improve the accuracy of image retrieval in the case of not decreasing the speed of image retrieval. Its mean precision is above 80 %.

关键词： intuitive fuzzy genetic algorithm color histogram image retrieval

image retrieval Based on Objects’ Spatial Orientation Relationships

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Wuhan University Journal of Natural Sciences 2005年第3期10卷 520-524页

作者： CHENChuan-bo LIQi-shen GONGWen-jie CollegeofComputerScienceandTechnology HuazhongUniversityofScienceandTechnologyWuhan430074HubeiChina

A new concept of characteristic scanning radial (CSR) is proposed for thesegmented image on the basis of two shape-specific points of its shape-objects. Subsequently, twocharacteristic attribute sequences (CAS) of relative distance and relative direction are derived torepresent the spatial orientation relationships among objects of the image. A novel image retrievalalgorithm is presented using these two CASs. The proposed retrieval approach not only satisfies thetransformational invariance, butalso attains the quantitative comparison of matching. Experimentsidentify the effectiveness and efficiency of the algorithm adequately.

关键词： image retrieval spatial relationship transformation invariant shape-specific point characteristic scanning radial characteristic attribute sequence

HBIR: Hypercube-Based image retrieval

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Journal of Computer Science & Technology 2012年第1期27卷 147-162页

作者： Hossein Ajorloo Abolfazl Lakdashti Department of Computer Engineering Sharif University of Technology Tehran Iran University College of Rouzbahan Sari Iran

In this paper, we propose a mapping from low level feature space to the semantic space drawn by the users through relevance feedback to enhance the performance of current content based image retrieval （CBIR） systems. The proposed approach makes a rule base for its inference and configures it using the feedbacks gathered from users during the life cycle of the system. Each rule makes a hypercube （HC） in the feature space corresponding to a semantic concept in the semantic space. Both short and long term strategies are taken to improve the accuracy of the system in response to each feedback of the user and gradually bridge the semantic gap. A scoring paradigm is designed to determine the effective rules and suppress the inefficient ones. For improving the response time, an HC merging approach and, for reducing the conflicts, an HC splitting method is designed. Our experiments on a set of 11 000 images from the Corel database show that the proposed approach can better describe the semantic content of images for image retrieval with respect to some existing approaches reported recently in the literature. Moreover, our approach can be better trained and is not saturated in long time, i.e., any feedback improves the precision and recall of the system. Another strength of our method is its ability to address the dynamic nature of the image database such that it can follow the changes occurred instantaneously and permanently by adding and dropping images.

关键词： image retrieval relevance feedback rule base hypercube (HC)

Incorporating Spatial Distribution Feature with Local Patterns for Content-Based image retrieval

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Chinese Journal of Electronics 2016年第5期25卷 873-879页

作者： WAN Shouhong JIN Peiquan XIA Yu YUE Lihua Institute of Compute Science and Technology University of Science and Technology of China Key Laboratory of Electromagnetic Space Information Chinese Academy of Sciences

Local patterns record the gray-level differences between a referenced pixel in an image and its surrounding pixels, which have been commonly used to describe the image features. However, traditional local patterns ignore the spatial distribution feature of texture information in images. We group the gray-level variations along three directions, i.e., horizontal, vertical, and diagonal directions. Each group is then merged into a Local spatial distribution pattern(LSDP) to represent the spatial distribution image feature. We also construct the LSDP patterns for gradient and filtered images, and finally form the Complete local spatial distribution pattern(CLSDP)descriptor to completely describe the texture image feature. Experiments on textural and natural image sets were conducted to compare our CLSDP-based image retrieval algorithm with four previous competitors. The results show that our method is superior to existing algorithms considering both average precision and recall.

关键词： Feature extraction or construction image retrieval Feature representation

An image retrieval Method Using DCT Features

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Journal of Computer Science & Technology 2002年第6期17卷 865-873页

作者：樊昀王润生 ATRNationalLab NationalUniversityofDefenseTechnologyChangsha410078P.R.China

In this paper a new image representation for compressed domain image re-trieval and an image retrieval system are presented. To represent images compactly and hi-erarchically, multiple features such as color and texture features directly extracted from DCTcoefficients are structurally organized using vector quantization. To train the codebook, a newMinimum Description Length vector quantization algorithm is used and it automatically decidesthe number of code words. To compare two images using the proposed representation, a newefficient similarity measure is designed. The new method is applied to an image database with1,005 pictures. The results demonstrate that the method is better than two typical histogrammethods and two DCT-based image retrieval methods.

关键词： image retrieval vector quantization color histogram DCT

Hierarchical deep hashing for image retrieval

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Frontiers of Computer Science 2017年第2期11卷 253-265页

作者： Ge SONG Xiaoyang TAN College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 211106 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 211106 China

We present a new method to generate efficient multi-level hashing codes for image retrieval based on the deep siamese conv01utional neural network （DSCNN）. Con- ventional deep hashing methods trade off the capability of capturing highly complex and nonlinear semantic informa- tion of images against very compact hash codes, usually lead- ing to high retrieval efficiency but with deteriorated accuracy. We alleviate the restrictive compactness requirement of hash codes by extending them to a two-level hierarchical coding scheme, in which the first level aims to capture the high-level semantic information extracted by the deep network using a rich encoding strategy, while the subsequent level squeezes them to more global and compact codes. At running time, we adopt an attention-based mechanism to select some of its most essential bits specific to each query image for retrieval instead of using the full hash codes of the first level. The attention-based mechanism is based on the guides of hash codes generated by the second level, taking advantage of both local and global properties of deep features. Experimental re- suits on various popular datasets demonstrate the advantages of the proposed method compared to several state-of-the-art methods.

关键词： image retrieval deep hashing hierarchical deep hashing

Medical Sign Recognition of Lung Nodules Based on image retrieval with Semantic Features and Supervised Hashing

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Journal of Computer Science & Technology 2017年第3期32卷 457-469页

作者： Juan-Juan Zhao Ling Pan Peng-Fei Zhao Kiao-Xian Tang College of Computer Science and Technology Taiyuan University of Technology Taiyuan 030024 China Shanxi Provincial People's Hospital Taiyuan 030012 China

Sign recognition is important for identifying benign and malignant nodules. This paper proposes a new sign recognition method based on image retrieval for lung nodules. First, we construct a deep learning framework to extract semantic features that can effectively represent sign information. Second, we translate the high-dimensional image features into compact binary codes with principal component analysis （PCA） and supervised hashing. Third, we retrieve similar lung nodule images with the presented adaptive-weighted similarity calculation method. Finally, we recognize nodule signs from the retrieval results, which can also provide decision support for diagnosis of lung lesions. The proposed method is validated on the publicly available databases： lung image database consortium and image database resource initiative （LIDC-IDRI） and lung computed tomography （CT） imaging signs （LISS）. The experimental results demonstrate our retrieval method substantially improves retrieval performance compared with those using traditional Hamming distance, and the retrieval precision can achieve 87.29%when the length of hash code is 48 bits. The entire recognition rate on the basis of the retrieval results can achieve 93.52%. Moreover, our method is also effective for real-life diagnosis data.

关键词： lung nodule medical sign recognition image retrieval supervised hashing adaptive weight

image retrieval with Text Manipulation by Local Feature Modification

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Journal of Donghua University(English Edition) 2023年第4期40卷 404-409页

作者：查剑宏燕彩蓉张艳婷王俊 College of Computer Science and Technology Donghua UniversityShanghai 201620China College of Fashion and Design Donghua UniversityShanghai 200051China

The demand for image retrieval with text manipulation exists in many fields, such as e-commerce and Internet search. Deep metric learning methods are used by most researchers to calculate the similarity between the query and the candidate image by fusing the global feature of the query image and the text feature. However, the text usually corresponds to the local feature of the query image rather than the global feature. Therefore, in this paper, we propose a framework of image retrieval with text manipulation by local feature modification(LFM-IR) which can focus on the related image regions and attributes and perform modification. A spatial attention module and a channel attention module are designed to realize the semantic mapping between image and text. We achieve excellent performance on three benchmark datasets, namely Color-Shape-Size(CSS), Massachusetts Institute of Technology(MIT) States and Fashion200K(+8.3%, +0.7% and +4.6% in R@1).

关键词： image retrieval text manipulation attention local feature modification

image retrieval with High-Dimension Triangle Matching 36

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Image Retrieval with High-Dimension Triangle Matching