MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

A computer-aided diagnosis (CAD) system based on mammograms enables early breast cancer detection, diagnosis, and treatment. However, the accuracy of the existing CAD systems remains unsatisfactory. This paper explores a breast CAD method based on feature fusion with convolutional neural network (CNN) deep features. First, we propose a mass detection method based on CNN deep features and unsupervised extreme learning machine (ELM) clustering. Second, we build a feature set fusing deep features, morphological features, texture features, and density features. Third, an ELM classifier is developed using the fused feature set to classify benign and malignant breast masses. Extensive experiments demonstrate the accuracy and efficiency of our proposed mass detection and breast cancer classification method.

/pdf/breast-cancer-detection-using-extreme-learning-machine-based-i1ngxxd77n.pdf

Breast Cancer Detection Using Extreme Learning Machine Based on Feature Fusion With CNN Deep Features

An evolutionary gravitational search-based feature selection

The paper describes the usage of self-learning Hierarchical LSTM technique for classifying hatred and trolling contents in social media code-mixed data. The Hierarchical LSTM-based learning is a novel learning architecture inspired from the neural learning models. The proposed HLSTM model is trained to identify the hatred and trolling words available in social media contents. The proposed HLSTM systems model is equipped with self-learning and predicting mechanism for annotating hatred words in transliteration domain. The Hindi–English data are ordered into Hindi, English, and hatred labels for classification. The mechanism of word embedding and character-embedding features are used here for word representation in the sentence to detect hatred words. The method developed based on HLSTM model helps in recognizing the hatred word context by mining the intention of the user for using that word in the sentence. Wide experiments suggests that the HLSTM-based classification model gives the accuracy of 97.49% when evaluated against the standard parameters like BLSTM, CRF, LR, SVM, Random Forest and Decision Tree models especially when there are some hatred and trolling words in the social media data.

/pdf/hatred-and-trolling-detection-transliteration-framework-1v29w8b1k6.pdf

Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text

Efficient boosted grey wolf optimizers for global search and kernel extreme learning machine training

Image steganalysis is the process of detecting the availability of hidden messages in the cover images. Therefore, it may be considered as a classification problem which categorizes an image either into a cover images or a stego image. Feature selection is one of the important phases of image steganalysis which can increase its computational efficiency and performance. In this paper, a novel levy flight-based grey wolf optimization has been introduced which is used to select the prominent features for steganalysis algorithm from a set of original features. For the same, SPAM and AlexNet have been used to generate the high dimensional features. Furthermore, the random forest classifier is used to classify the images over selected features into cover images and stego images. The experimental results show that the proposed levy flight-based grey wolf optimization shows preferable convergence precision and effectively reduces the irrelevant and redundant features while maintaining the high classification accuracy as compared to other feature selection methods.

Feature selection for image steganalysis using levy flight-based grey wolf optimization

Optimized Time Synchronized Multilayer MAC Protocol for WSN Using Relay Nodes.

An efficient and robust approach for biomedical image retrieval using Zernike moments

Computed tomography (CT) approach is extensively utilized in clinical diagnoses. However, X-ray residue in human body may introduce somatic damage such as cancer. Owing to radiation risk, research ...

Low-dose CT image reconstruction using gain intervention-based dictionary learning

The low-dose X-ray Computed Tomography (CT) is one of the most effective and indispensable imaging tools for clinical diagnosis. The reduced number of photons in low-dose X-ray CT imaging introduce...

Shailendra Tiwari

Papers

Feature selection for image steganalysis using levy flight-based grey wolf optimization

Optimized Time Synchronized Multilayer MAC Protocol for WSN Using Relay Nodes.

An efficient and robust approach for biomedical image retrieval using Zernike moments

Low-dose CT image reconstruction using gain intervention-based dictionary learning

Fourth-order partial differential equations based anisotropic diffusion model for low-dose CT images