Called Content Based Image Retrieval Research Articles

Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval

It is very pleasing for human health that medical knowledge has increased and the technological infrastructure improves medical systems. The widespread use of medical imaging devices has been instrumental in saving lives by allowing early diagnosis of many diseases. These medical images are stored in large databases for many purposes. These datasets are used when a suspicious diagnostic case is encountered or to gain experience for inexperienced radiologists. To fulfill these tasks, images similar to one query image are searched from within the large dataset. Accuracy and speed are vital for this process, which is called content-based image retrieval (CBIR). In the literature, the best way to perform a CBIR system is by using hash codes. This study provides an effective hash code generation method based on feature selection-based downsampling of deep features extracted from medical images. Firstly, pre-hash codes of 256-bit length for each image are generated using a pairwise siamese network architecture that works based on the similarity of two images. Having a pre-hash code between -1 and 1 makes it very easy to generate hash code in hashing algorithms. For this reason, all activation functions of the proposed convolutional neural network (CNN) architecture are selected as hyperbolic tanh. Finally, neighborhood component analysis (NCA) feature selection methods are used to convert pre-hash code to binary hash code. This also downsamples the hash code length to 32-bit, 64-bit, or 96-bit levels. The performance of the proposed method is evaluated using NEMA MRI and NEMA CT datasets.

New Approach on the Techniques of Content-Based Image Retrieval (CBIR) Using Color, Texture and Shape Features

Digital image collection as rapidly increased along with the development of computer network. Image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and color similarity. Retrieving images based on the content which is color, texture, and shape is called content based image retrieval (CBIR). The content is actually the feature of an image and these features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features. There are two kinds of content based image retrieval which are general image retrieval and application specific image retrieval. For the general image retrieval, the goal of the query is to obtain images with the same object as the query. Such CBIR imitates web search engines for images rather than for text. For application specific, the purpose tries to match a query image to a collection of images of a specific type such as fingerprints image and x-ray. In this paper, the general architecture, various functional components, and techniques of CBIR system are discussed. CBIR techniques discussed in this paper are categorized as CBIR using color, CBIR using texture, and CBIR using shape features. This paper also describe about the comparison study about color features, texture features, shape features, and combined features (hybrid techniques) in terms of several parameters. The parameters are precision, recall and response time.

Content Based Image Retrieval from Auto Encoders Using Keras and Tensor Flow Python API a Deep Learning Technique

As the technology is evolving new methods and techniques are determined and implemented in a smart way to improve and achieve a greater insight in this smart era. The retrieval of image is popularly growing in this emerging trend. In this paper we have used how to build a very simple image retrieval system using a special type of Neural Network called auto encoders. Here the images can be retrieved with visual contents textures, shape and this method of image retrieval is called content based image retrieval.

Online product search using gray level co-occurrence matrix, color moments, and histogram of oriented gradients for content based image retrieval

Usually an information retrieval system uses text as its query for retrieving the results. However, the system can also use images, as opposed to text, for the search query. This technique commonly is called content based image retrieval. In this paper, we explore the retrieval of fashion products such as hats, sneakers, t-shirts, flat shoes, and dresses from images using color, texture, and shape features to represent the characteristics of the query images. The color, texture, and shape features are color moments, gray level co-occurrence matrix, and histogram of oriented gradients, respectively. The feature extraction process produces vectors with statistical feature value which can then be clustered to retrieve images with high degree of similarity. Comparing those feature representations for the retrieval system, it is shown that shape feature is the best representation.

Knn And Steerable Pyramid Based Enhanced Content Based Image Retrieval Mechanism

Recently, digital content has become a significant and inevitable asset of or any enterprise and the need for visual content management is on the rise as well. There has been an increase in attention towards the automated management and retrieval of digital images owing to the drastic development in the number and size of image databases. A significant and increasingly popular approach that aids in the retrieval of image data from a huge collection is called Content-based image retrieval (CBIR). Content-based image retrieval has attracted voluminous research in the last decade paving way for development of numerous techniques and systems besides creating interest on fields that support these systems. CBIR indexes the images based on the features obtained from visual content so as to facilitate speedy retrieval. Content based image retrieval from large resources has become an area of wide interest nowadays in many applications. In this thesis work, we present a steerable pyramid based image retrieval system that uses color, contours and texture as visual features to describe the content of an image region. To speed up retrieval and similarity computation, the database images are classified and the extracted regions are clustered according to their feature vectors using KNN algorithm We have used steerable pyramid to extract texture features from query image and classified database images and store them in feature features. Therefore to answer a query our system does not need to search the entire database images; instead just a number of candidate images are required to be searched for image similarity. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time.

Analysis on Shape Image Retrieval Using DNN and ELM Classifiers for MRI Brain Tumor Images

The problem of searching a digital image in a very huge database is called Content Based Image Retrieval (CBIR). Shape is a significant cue for describing objects. In this paper, we have developed a shape feature extraction of MRI brain tumor image retrieval. We used T1 weighted image of MRI brain tumor images. There are two modules: feature extraction process and classification. First, the shape features are extracted using techniques like Scale invariant feature transform (SIFT), Harris corner detection and Zernike Moments. Second, the supervised learning algorithms like Deep neural network (DNN) and Extreme learning machine (ELM) are used to classify the brain tumor images. Experiments are performed using 1000 brain tumor images. In the performance evaluation, sensitivity, specificity, accuracy, error rate and f-measure are five measures are used. The Experiment result shows that highest average accuracy has got at Zernike Moments- 99%. So, Zernike Moments are better than SIFT and Harris corner detection techniques. The average time taken for DNN- 0.0901 sec, ELM- 0.0218 sec. So, ELM classifier is better than DNN. It increases the retrieval time and improves the retrieval accuracy significantly.

Image Retrieval Berdasarkan Fitur Warna, Bentuk, dan Tekstur

Along with the times, information retrieval is no longer just on textual data, but also the visual data. The technique was originally used is Text-Based Image Retrieval (TBIR), but the technique still has some shortcomings such as the relevance of the picture successfully retrieved, and the specific space required to store meta-data in the image. Seeing the shortage of Text-Based Image Retrieval techniques, then other techniques were developed, namely Image Retrieval based on content or commonly called Content Based Image Retrieval (CBIR). In this research, CBIR will be discussed based on color, shape and texture using a color histogram, Gabor and SIFT. This study aimed to compare the results of image retrieval with some of these techniques. The results obtained are by combining color, shape and texture features, the performance of the system can be improved.