Comparison and Combination of Textual and Visual Features for Interactive Cross-Language Image Retrieval

2005 ◽  
pp. 793-804 ◽  
Author(s):  
Pei-Cheng Cheng ◽  
Jen-Yuan Yeh ◽  
Hao-Ren Ke ◽  
Been-Chian Chien ◽  
Wei-Pang Yang
Keyword(s):  
Image Retrieval ◽  
Visual Features ◽  
Cross Language

scholarly journals Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Xian-Hua Han ◽  
Yen-Wei Chen
Keyword(s):  
Image Retrieval ◽  
Imaging Modality ◽  
Kernel Functions ◽  
Visual Features ◽  
Color Intensity ◽  
Global Features ◽  
Retrieval Task ◽  
Block Based ◽  
Cross Language ◽  
Textual Feature

We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.

Enhanced Content-Based Image Retrieval Using Information Oriented Angle-Based Local Tri-Directional Weber Patterns

2021 ◽  
pp. 2150046
Author(s):  
Gangavarapu Venkata Satya Kumar ◽  
Pillutla Gopala Krishna Mohan
Keyword(s):  
Image Retrieval ◽  
Computer Applications ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
The Novel ◽  
Image Content ◽  
Query Image ◽  
Proposed Model ◽  
Maximum Mutual Information ◽  
Basic Features

In diverse computer applications, the analysis of image content plays a key role. This image content might be either textual (like text appearing in the images) or visual (like shape, color, texture). These two image contents consist of image’s basic features and therefore turn out to be as the major advantage for any of the implementation. Many of the art models are based on the visual search or annotated text for Content-Based Image Retrieval (CBIR) models. There is more demand toward multitasking, a new method needs to be introduced with the combination of both textual and visual features. This paper plans to develop the intelligent CBIR system for the collection of different benchmark texture datasets. Here, a new descriptor named Information Oriented Angle-based Local Tri-directional Weber Patterns (IOA-LTriWPs) is adopted. The pattern is operated not only based on tri-direction and eight neighborhood pixels but also based on four angles [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]. Once the patterns concerning tri-direction, eight neighborhood pixels, and four angles are taken, the best patterns are selected based on maximum mutual information. Moreover, the histogram computation of the patterns provides the final feature vector, from which the new weighted feature extraction is performed. As a new contribution, the novel weight function is optimized by the Improved MVO on random basis (IMVO-RB), in such a way that the precision and recall of the retrieved image is high. Further, the proposed model has used the logarithmic similarity called Mean Square Logarithmic Error (MSLE) between the features of the query image and trained images for retrieving the concerned images. The analyses on diverse texture image datasets have validated the accuracy and efficiency of the developed pattern over existing.

scholarly journals Color Based Image Retrieval by Combining Various Features

2019 ◽  
Vol 9 (2) ◽  
pp. 454-460
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Visual Features ◽  
Semantic Features ◽  
Retrieval Rate ◽  
Image Retrieval System ◽  
Mathematical Formulas ◽  
Image Set ◽  
Different Color ◽  
Image Collection

Content based image retrieval system retrieve the images according to the strong feature related to desire as color, texture and shape of an image. Although visual features cannot be completely determined by semantic features, but still semantic features can be integrate easily into mathematical formulas. This paper is focused on retrieval of images within a large image collection, based on color projection by applying segmentation and quantification on different color models and compared for good result. This method is applied on different categories of image set and evaluated its retrieval rate in different models

Multimedia Image Retrieval System by Combining CNN With Handcraft Features in Three Different Similarity Measures

2020 ◽  
Vol 10 (1) ◽  
pp. 1-23
Author(s):  
Maher Alrahhal ◽  
Supreethi K.P.
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Retrieval System ◽  
Similarity Measures ◽  
Visual Features ◽  
Classification Problems ◽  
Bag Of Features ◽  
Image Retrieval System ◽  
Multimedia Image ◽  
Medical Dataset

The authors propose WNAHVF, a combined weighted and normalized AlexNet with handcrafted visual features for extracting features from images and using those vectors for image retrieval and classification. The authors test the WNAHVF method on two general datasets, Corel-1k and Corel-10k, and one medical dataset. The outcomes demonstrate combining Bag of Features and Local Neighbor patterns with AlexNet enhances the accuracy and gives better results in general and medical image datasets in retrieval and classification problems. This algorithm gives results that are superior to existing strategies.

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