A Novel Framework for Image Categorization and Automatic Annotation

Image classification and automatic annotation could be treated as effective solutions to enable keyword-based semantic image retrieval. Traditionally, they are investigated in different models separately. In this chapter, we propose a novel framework uniting image classification and automatic annotation by learning semantic concepts of image categories. To choose representative features, feature selection strategy is proposed and visual keywords are constructed, including discrete method and continuous method. Based on the selected features, the Integrated Patch (IP) model is proposed to describe the image category. As a generative model, the IP model describes the appearance of the combination of the visual keywords, considering the diversity of the object. The parameters are estimated by EM algorithm. The experimental results on Corel image dataset and Getty Image Archive demonstrate that the proposed feature selection and image description model are effective in image categorization and automatic image annotation, respectively.

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Natural Scene Image Annotation Using Local Semantic Concepts and Spatial Bag of Visual Words

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327906666160606152043 ◽

2016 ◽

Vol 6 (3) ◽

pp. 153-173

Author(s):

Yousef Alqasrawi

Keyword(s):

Image Annotation ◽

Bag Of Visual Words ◽

Natural Scene ◽

Visual Words ◽

Scene Image ◽

Semantic Concepts

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Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

Recent Patents on Computer Science ◽

10.2174/2213275912666181210165129 ◽

2019 ◽

Vol 12 (4) ◽

pp. 329-337 ◽

Cited By ~ 2

Author(s):

Venubabu Rachapudi ◽

Golagani Lavanya Devi

Keyword(s):

Feature Selection ◽

Image Classification ◽

Feature Selection Method ◽

Selection Method ◽

Lévy Flight ◽

Levy Flight ◽

Local Optima ◽

Histopathological Image ◽

Surf Features ◽

Histopathological Image Classification

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

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Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

Remote Sensing ◽

10.3390/rs11161933 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1933 ◽

Cited By ~ 5

Author(s):

Yangyang Li ◽

Ruoting Xing ◽

Licheng Jiao ◽

Yanqiao Chen ◽

Yingte Chai ◽

...

Keyword(s):

Image Classification ◽

State Of The Art ◽

Sample Selection ◽

Spatial Relations ◽

Speckle Noise ◽

Classification Performance ◽

Selection Strategy ◽

Training Set ◽

Polarimetric Synthetic Aperture Radar ◽

Unlabeled Sample

Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

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