Large-Scale Zero-Shot Image Classification from Rich and Diverse Textual Descriptions

We study the impact of using rich and diverse textual descriptions of classes for zero-shot learning (ZSL) on ImageNet. We create a new dataset ImageNet-Wiki that matches each ImageNet class to its corresponding Wikipedia article. We show that merely employing these Wikipedia articles as class descriptions yields much higher ZSL performance than prior works. Even a simple model using this type of auxiliary data outperforms state-of-the-art models that rely on standard features of word embedding encodings of class names. These results highlight the usefulness and importance of textual descriptions for ZSL, as well as the relative importance of auxiliary data type compared to the algorithmic progress. Our experimental results also show that standard zero-shot learning approaches generalize poorly across categories of classes.

A comparison of Image Enhancement Techniques for Recognizing and Classifying Automatically the Medical Images and implement on MRI brain Image

The amount of digital images that are produced in hospitals is increasing rapidly. Effective medical images can play an important role in aiding in diagnosis and treatment, they can also be useful in the education domain for healthcare students by explaining with these images will help them in their studies, new trends for image retrieval using automatic image classification has been investigated for the past few years. Medical image Classification can play an important role in diagnostic and teaching purposes in medicine. For these purposes different imaging modalities are used. There are many classifications created for medical images using both grey-scale and color medical images. In this paper, different algorithms in every step involved in medical image processing have been studied. One way is the algorithms of preprocessing step such as Median filter [1], Histogram equalization (HE) [2], Dynamic histogram equalization (DHE), and Contrast Limited Adaptive Histogram Equalization (CLAHE). Second way is the Feature Selection and Extraction step [3,4], such as Gray Level Co-occurrence Matrix(GLCM). Third way is the classification techniques step, which is divided into three ways in this paper, first one is texture classification techniques, second one is neural network classification techniques, and the third one is K-Nearest Neighbor classification techniques. In this paper, we have use MRI brain image to determine the area of tumor in brain. The steps started by preprocessing operation to the image before inputting it to algorithm. The image was converted to gray scale, later on remove film artifact using special algorithm, and then remove the Skull portions from the image without effect on white and gray matter of the brain using another algorithm, After that the image enhanced using optimized median filter algorithm and remove Impurities that produced from first and second steps.

Buildings detection in Satellite images

In this paper, we present a new algorithm to automate the detection and extraction of buildings from satellite images, this algorithm is distinguished since it overcomes some obstacles that limit detecting within other methods, such as the difference in shape, color, and height of buildings, and it doesn't need multi-spectral images or other complex and high cost images.

Developing an effective system for medical image classification using Group Sparsity and fuzzy enhancement

In this research we introduce a regularization based feature selection algorithm to benefit from sparsity and feature grouping properties and incorporate it into the medical image classification task. Using this group sparsity (GS) method, the whole group of features are either selected or removed. The basic idea in GS is to delete features that do not affect the retrieval process, instead of keeping them and giving these features small weights. Therefore, GS improves system by increasing accuracy of the results, plus reducing space and time requirements needed by the system.