Image Representation Using Stacked Colour Histogram

Image representation plays a vital role in the realisation of Content-Based Image Retrieval (CBIR) system. The representation is performed because pixel-by-pixel matching for image retrieval is impracticable as a result of the rigid nature of such an approach. In CBIR therefore, colour, shape and texture and other visual features are used to represent images for effective retrieval task. Among these visual features, the colour and texture are pretty remarkable in defining the content of the image. However, combining these features does not necessarily guarantee better retrieval accuracy due to image transformations such rotation, scaling, and translation that an image would have gone through. More so, concerns about feature vector representation taking ample memory space affect the running time of the retrieval task. To address these problems, we propose a new colour scheme called Stack Colour Histogram (SCH) which inherently extracts colour and neighbourhood information into a descriptor for indexing images. SCH performs recurrent mean filtering of the image to be indexed. The recurrent blurring in this proposed method works by repeatedly filtering (transforming) the image. The output of a transformation serves as the input for the next transformation, and in each case a histogram is generated. The histograms are summed up bin-by-bin and the resulted vector used to index the image. The image blurring process uses pixel’s neighbourhood information, making the proposed SCH exhibit the inherent textural information of the image that has been indexed. The SCH was extensively tested on the Coil100, Outext, Batik and Corel10K datasets. The Coil100, Outext, and Batik datasets are generally used to assess image texture descriptors, while Corel10K is used for heterogeneous descriptors. The experimental results show that our proposed descriptor significantly improves retrieval and classification rate when compared with (CMTH, MTH, TCM, CTM and NRFUCTM) which are the start-of-the-art descriptors for images with textural features.

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Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms

International Journal of Biomedical Imaging ◽

10.1155/2011/241396 ◽

2011 ◽

Vol 2011 ◽

pp. 1-7 ◽

Cited By ~ 6

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.

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Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/137 ◽

2020 ◽

Author(s):

Xinxun Xu ◽

Muli Yang ◽

Yanhua Yang ◽

Hao Wang

Keyword(s):

Image Retrieval ◽

Semantic Information ◽

Semantic Space ◽

Semantic Knowledge ◽

Visual Features ◽

Semantic Features ◽

Retrieval Task ◽

Common Space ◽

Low Dimensional ◽

Domain Independent

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a specific cross-modal retrieval task for searching natural images given free-hand sketches under the zero-shot scenario. Most existing methods solve this problem by simultaneously projecting visual features and semantic supervision into a low-dimensional common space for efficient retrieval. However, such low-dimensional projection destroys the completeness of semantic knowledge in original semantic space, so that it is unable to transfer useful knowledge well when learning semantic features from different modalities. Moreover, the domain information and semantic information are entangled in visual features, which is not conducive for cross-modal matching since it will hinder the reduction of domain gap between sketch and image. In this paper, we propose a Progressive Domain-independent Feature Decomposition (PDFD) network for ZS-SBIR. Specifically, with the supervision of original semantic knowledge, PDFD decomposes visual features into domain features and semantic ones, and then the semantic features are projected into common space as retrieval features for ZS-SBIR. The progressive projection strategy maintains strong semantic supervision. Besides, to guarantee the retrieval features to capture clean and complete semantic information, the cross-reconstruction loss is introduced to encourage that any combinations of retrieval features and domain features can reconstruct the visual features. Extensive experiments demonstrate the superiority of our PDFD over state-of-the-art competitors.

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An Efficient Image Retrieval Based on Fusion of Low-Level Visual Features

10.20944/preprints201812.0022.v1 ◽

2018 ◽

Author(s):

Atif Nazir ◽

Kashif Nazir

Keyword(s):

Feature Extraction ◽

Image Retrieval ◽

Feature Fusion ◽

Image Representation ◽

Feature Space ◽

Ground Truth ◽

Image Feature ◽

Visual Features ◽

Low Level ◽

Time Required

Due to an increase in the number of image achieves, Content-Based Image Retrieval (CBIR) has gained attention for research community of computer vision. The image visual contents are represented in a feature space in the form of numerical values that is considered as a feature vector of image. Images belonging to different classes may contain the common visuals and shapes that can result in the closeness of computed feature space of two different images belonging to separate classes. Due to this reason, feature extraction and image representation is selected with appropriate features as it directly affects the performance of image retrieval system. The commonly used visual features are image spatial layout, color, texture and shape. Image feature space is combined to achieve the discriminating ability that is not possible to achieve when the features are used separately. Due to this reason, in this paper, we aim to explore the  low-level feature combination that are based on color and shape features. We selected color moments and color histogram to represent color while shape is represented by using invariant moments. We selected this combination, as these features are reported intuitive, compact and robust for image representation. We evaluated the performance of our proposed research by using the Corel, Coil and Ground Truth (GT) image datasets. We evaluated the proposed low-level feature fusion by calculating the precision, recall and time required for feature extraction. The precision, recall and feature extraction values obtained from the proposed low-level feature fusion outperforms the existing research of CBIR.

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Content-based image retrieval using a combination of visual features and eye tracking data

Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications - ETRA '10 ◽

10.1145/1743666.1743675 ◽

2010 ◽

Cited By ~ 11

Author(s):

Zhen Liang ◽

Hong Fu ◽

Yun Zhang ◽

Zheru Chi ◽

Dagan Feng

Keyword(s):

Eye Tracking ◽

Image Retrieval ◽

Content Based Image Retrieval ◽

Visual Features ◽

Tracking Data

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Learning optimal visual features from Web sampling in online image retrieval

2008 IEEE International Conference on Acoustics, Speech and Signal Processing ◽

10.1109/icassp.2008.4517838 ◽

2008 ◽

Cited By ~ 3

Author(s):

Sabrina Tollari ◽

Herve Glotin

Keyword(s):

Image Retrieval ◽

Visual Features

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The sparse image representation for automated image retrieval

2008 15th IEEE International Conference on Image Processing ◽

10.1109/icip.2008.4711682 ◽

2008 ◽

Cited By ~ 8

Author(s):

Pavel Praks ◽

Radek Kucera ◽

Ebroul Izquierdo

Keyword(s):

Image Retrieval ◽

Image Representation

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

International Journal of Image and Graphics ◽

10.1142/s0219467821500467 ◽

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.

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Color Based Image Retrieval by Combining Various Features

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b3163.129219 ◽

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

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Deep Learning Triplet Ordinal Relation Preserving Binary Code for Remote Sensing Image Retrieval Task

Remote Sensing ◽

10.3390/rs13234786 ◽

2021 ◽

Vol 13 (23) ◽

pp. 4786

Author(s):

Zhen Wang ◽

Nannan Wu ◽

Xiaohan Yang ◽

Bingqi Yan ◽

Pingping Liu

Keyword(s):

Remote Sensing ◽

Image Retrieval ◽

Large Scale ◽

Nearest Neighbor ◽

Binary Code ◽

Satellite Observation ◽

Cross Entropy ◽

Retrieval Task ◽

Search Tasks ◽

Low Dimensional

As satellite observation technology rapidly develops, the number of remote sensing (RS) images dramatically increases, and this leads RS image retrieval tasks to be more challenging in terms of speed and accuracy. Recently, an increasing number of researchers have turned their attention to this issue, as well as hashing algorithms, which map real-valued data onto a low-dimensional Hamming space and have been widely utilized to respond quickly to large-scale RS image search tasks. However, most existing hashing algorithms only emphasize preserving point-wise or pair-wise similarity, which may lead to an inferior approximate nearest neighbor (ANN) search result. To fix this problem, we propose a novel triplet ordinal cross entropy hashing (TOCEH). In TOCEH, to enhance the ability of preserving the ranking orders in different spaces, we establish a tensor graph representing the Euclidean triplet ordinal relationship among RS images and minimize the cross entropy between the probability distribution of the established Euclidean similarity graph and that of the Hamming triplet ordinal relation with the given binary code. During the training process, to avoid the non-deterministic polynomial (NP) hard problem, we utilize a continuous function instead of the discrete encoding process. Furthermore, we design a quantization objective function based on the principle of preserving triplet ordinal relation to minimize the loss caused by the continuous relaxation procedure. The comparative RS image retrieval experiments are conducted on three publicly available datasets, including UC Merced Land Use Dataset (UCMD), SAT-4 and SAT-6. The experimental results show that the proposed TOCEH algorithm outperforms many existing hashing algorithms in RS image retrieval tasks.

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