OCR-based image features for biomedical image and article classification
Hagit ShatkayRamya NarayanaswamySantosh S. NagaralNa HarringtonRohith MVGowri SomanathRyan TarpineKyle SchutterTimothy G. JohnstoneDorothea BlosteinSorin IstrailChandra Kambhamettu
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Abstract:
Images form a significant and useful source of information in published biomedical articles, which is still under-utilized in biomedical document classification and retrieval. Much current work on biomedical image retrieval and classification employs simple, standard image features such as gray scale histograms and edge direction to represent and classify images. We have used such features as well to classify images in our early work [5], where we used image-class-tags to represent and classify articles.Keywords:
Contextual image classification
With extensive usage of multimedia databases in real time applications, there arises a great need for developing efficient techniques to find the images from huge digital libraries. To find an image from a database, every image is represented with certain features. Texture and color are two important visual features of an image. In this paper we compare and analyze performance of image retrieval using texture and color features. Further we propose and implement an efficient image retrieval technique using both texture and color features of an image. Experimental evaluation is carried out on Wang image database having 1000 unique images consisting of 10 classes of images.
Content-Based Image Retrieval
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Image classification is perhaps the most important part of digital image analysis. Retrieval patternbased learning is the most effective that aim to establish the relationship between the current and previous query sessions by analyzing image retrieval patterns. We propose a new feedback based and content based image retrieval system. Content based image retrieval from large resources has become an area of wide interest nowadays in many applications. In this paper we present content-based image retrieval system that uses color and texture as visual features to describe the content of an image region. Our contribution are we use Gabor filters to extract texture features from arbitrary shaped regions separated from an image after segmentation to increase the system effectiveness. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from color the whole image, and features extracted from Texture some image regions. That approach is more effective and efficient way for image retrieval.
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Technology brings images as a communication media for humans. Image communication used today in many fields such as education, media, healthcare and in other domains. Based on image retrieval user input selection one of the most powerful technique and has been an active research direction for the couple of years. Various features are used for image retrieval. Most of the retrieval technique used image multi level features for the user feed back. But this features are used retrieve only image low level features and it never retrieve the image based on the content. In the proposed technique content based retrieval used for retrieve the images which is most similar to the input image. Here we are taking the texture, color and shape of the image and stored in the database. When the user asks query, then it will be matched with the database and retrieve the image. It will retrieve the exact image by comparing the texture, color and shape.
Content-Based Image Retrieval
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Content-Based Image Retrieval
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A large number of algorithms have been proposed for texture image retrieval and analysis. While each algorithm has its own characteristics and is suitable for indexing some particular categories of images, there is a lacking of an effective strategy to integrate these algorithms to maximize the retrieval performance based on the algorithms available. Our first step in this direction is to build up a texture image retrieval system which adaptively chooses the "right" transform to query texture database. Experiments on the Brodatz texture set show that the adaptive image retrieval system significantly outperforms the commonly used single-algorithm based ones.
Texture compression
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Retrieving images from the large databases has always been one challenging problem in the area of image retrieval while maintaining the higher accuracy and lower computational time. Texture defines the roughness of a surface. For the last two decades due to the large extent of multimedia database, image retrieval has been a hot issue in image processing. Texture images are retrieved in a variety of ways. This paper presents a survey on various texture image retrieval methods. It provides a brief comparison of various texture image retrieval methods on the basis of retrieval accuracy and computation time. Image retrieval techniques vary with feature extraction methods and various distance measures. In this paper, we present a survey on various texture feature extraction methods by applying tertrolet transform. This survey paper facilitates the researchers with background of progress of image retrieval methods that will help researchers in the area to select the best method for texture image retrieval appropriate to their requirements.
Content-Based Image Retrieval
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Texture (cosmology)
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The retrieval of images depending on content is a recurrent research topic in medical imaging. Most CBIR systems are designed to help physicians in the diagnostic of the pathologies. Image retrieval according to the content of the texture features can be performed through various methods developed so far. Local texture features are very beneficial for the analysis of the texture, thus, they are extensively used in image retrieval. The original LBP is improved in this paper with a new addition for CBIR called uniform extended local ternary pattern (UELTP). The method decomposes the image into objects; local texture features are extracted and stored into n-dimensional texture feature vectors. Then, the images are frequently obtained from a huge database dedicated for images using these vectors. In this paper, the performance of LBP descriptor, LTP and ELTP are evaluated for CBIR. According to the results, uniform extended local ternary pattern more accurate than other descriptors in terms of image retrieval.
Local Binary Patterns
Content-Based Image Retrieval
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Content-Based Image Retrieval (CBIR) system is emerging as an important research area, users can search and retrieve images based on their properties such as shape, color and texture from the image database. Usually texture-based image retrieval just consider an original image of coarseness, contrast and roughness, actually there is much texture information in the edge image. This paper proposed a novel approach to retrieve images by texture characterization using a composition of edge information and co-occurrence matrix properties. The proposed method gives encouraging results when comparing its retrieval performance to that of the traditional co-occurrence matrices and Yaopsilas approach.
Texture (cosmology)
Content-Based Image Retrieval
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The content based image retrieval (CBIR) is one of the most popular, rising research area of the digital image processing. In this technique which uses visual content to search image from large scale image database and its main goal of CBIR is to extract visual content of a medical image. Image retrieval based on a query image is necessary for effective and efficient use the information that is stored in medical image database. The system stores medical image along with its information (parameter) related to that medical image. Retrieving can be done by extracting most informative texture feature which can be extracted by using Gray-level-co-occurrence matrix (GLCM). This allows the retrieval of images by performing flexible queries on the database. Our main purpose is to get high accuracy of a medical image obtained by retrieving techniques. After retrieving the medical image, which can be further segmented to obtain the particular region of the medical image which is affected by any diseases.
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In recent years, there has been a growing interest in developing effective methods for searching large image databases based on image content. The interest in image search algorithms has grown out of the necessity of managing large image databases that are now commonly available on removable storage media and wide area networks. The objective of this paper is to present a novel image retrieval system based on texture features extraction. The system works with the subdatabases that include images with the same content. Subdatabases are described with centroids. Comparison with conventional retrieval systems show that new system is faster and more accurate.
Content-Based Image Retrieval
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