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Interest in the digital images has increased a lot over the last few years, but the process of locating a desired image in such a large and diverse image collection becomes very difficult. Traditionally text in different languages is used for efficient retrieval of images; it has several drawbacks such as language constraint and subjectivity of human perception.
Nowadays, content-based image-retrieval techniques constitute powerful tools for archiving and mining of large remote sensing image databases. High spatial resolution images are complex and differ widely in their content, even in the same category. All images are more or less textured and structured. If the image to recognize is somewhat or very structured, a shape feature will be somewhat or very...
This paper presents a novel approach, named bag-of-bags of words (BBoW), to address the problem of Content-Based Image Retrieval (CBIR) from image databases. The proposed bag-of-bags of words model extends the classical bag-of-words (BoW) model. An image is represented as a connected graph of local features on a regular grid. Then irregular partitions (subgraphs) of images are further built via Normalized...
Image Retrieval is a system which extracts the relevant set of images and matches with query image from large collection of dataset. It is used in variety of domains including finger print identification, biodiversity information system, digital library, medical imaging etc. An effective and efficient system is required to improve its retrieval performance. In CBIR, the images are indexed according...
In content-based image retrieval, and for this critical issue of image feature fusion, paper proposes a new method to determine the weights for multi-feature fusion. In this paper, color histogram, color correlogram, gray level co-occurrence matrix, Tamura and Hu moments, this five kinds of feature extraction method was adopted. Firstly, use these five features conducted single feature retrieval on...
The timely and accurate identification of plant species is a persistent challenge as pressure from human activity threatens global flora biodiversity. Most existing research on computer based plant species identification has focused on using leaf contour, signature and spectral analysis techniques alongside textural properties of the leaf lamina. However, these global feature based methods often suffer...
This paper addresses the problem of fast similar image retrieval, especially for large-scale datasets with millions of images. We present a new framework which consists of two dependent algorithms. First, a new feature is proposed to represent images, which is dubbed compact feature based clustering (CFC). For each image, we first extract cluster centers of local features, and then calculate distribution...
A large collection of images is referred to as image database. An image database is a system where image data are integrated, coupled and stored. Image data include the raw images, image set and information derived from images by automated or computer assisted image analysis. In a small collection of images, simple browsing can identify an image. In text-based retrieval, images are retrieved using...
In this paper, we present the results of applying global morphological texture descriptors to the problem of content-based remote sensing image retrieval. Specifically, we explore the potential of recently developed multiscale texture descriptors, namely, the circular covariance histogram and the rotation-invariant point triplets. Moreover, we introduce a couple of new descriptors, exploiting the...
This paper approaches the image retrieval system on the base of visual features local region RBIR (region-based image retrieval). First of all, the paper presents a method for extracting the interest points based on Harris-Laplace to create the feature region of the image. Next, in order to reduce the storage space and speed up query image, the paper builds the binary signature structure to describe...
In this paper, HSV based text on histogram (HSV-TH) is proposed for content based image retrieval (CBIR). The HSV-MT is proposed in contrast to the RGB based text on histogram (RGB-TH). The proposed HSV-TH method is based on Julesz's textons theory, and it works directly on nature images as shape descriptor and a color texture descriptor. HSV-TH integrates the advantages of co-occurrence matrix and...
This paper presents a simple yet efficient image retrieval technique that defines image feature descriptors using localized multi-text on histogram. The proposed technique extracts a unique feature vector for each image in the image database based on its shape, texture and color. First, the image is divided into smaller equal size blocks and then for each block texture orientation is computed independently...
Content based image retrieval (CBIR) system is a database management system for retrieval of images based on the similarity of image content with the query image. In the proposed CBIR system, Tamura texture features are extracted as image content. To measure similarity of query image with images in database, a fuzzified distance measure, fuzzy hamming distance (FHD), is used. The database is sorted...
SIFT is a well-known feature extract algorithm for image matching. Due to the high dimensionality caused by the feature descriptor, it is too time consuming for real-time image retrieval application with kd-tree. To fast the image retrieval process, we deal with it in two aspects. Firstly, a novel method is proposed to reduce the dimensionality of the sift descriptor, which significantly drops the...
Digital images have many applications in different fields like medical imaging and diagnostics, weather forecasting, space research, military etc. The number of images available and their wide variety increases with the ease of acquiring, storing and sharing digital images due to the advances in technology. As a result, the significance of image retrieval algorithms and systems has been considerably...
Magnetic resonance imaging allows a number of imaging techniques and protocols that can be used to capture the different aspects of the cardiac function and structure. The produced amount of data is huge and its classification and/or retrieval based on its visual content are necessary for educational and training purposes. In this work, we propose a method for classification and retrieving cardiac...
Traditional image retrieval depends on the images embedded in text messages, text description of the limitations of image content, resulting in low quality of image retrieval. The local information extracted image itself, the use of local features LSH image matching algorithm, memory requirements has led to a linear growth. To overcome these shortcomings, then propose the method of image retrieval...
With the existing feature weighting methods of image retrieval field, it was impossible to use the fact that images have different key features depending on their classes because the same weight is applied to every image class. We propose a method of indexing features of each class in order of importance and giving them relevant weights, which can be applied to image retrieval. We designed a simple...
In order to overcome """"semantic gap"""" between bottom features and high-level semantic in the image retrieval, this paper introduces the echo state network to strengthen the mapping between the high-level vision content and the bottom visual feature and designs a feedback category screening strategy. We extract the feature of the queried image and get the...
In this paper we will present our experiments regarding Content Based Image Retrieval for a public database of images containing buildings. We have based our research on the possible improvements brought by splitting the image into regions and computing a certain descriptor for each one of these. Our goal was to observe the influence of local descriptors on buildings recognition. Experimental tests...
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