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In this paper, we propose an incremental evolution scheme within collective network of (evolutionary) binary classifiers (CNBC) framework to address the problem of incremental learning and to achieve a high retrieval performance for content-based image retrieval (CBIR). The proposed CNBC framework can still function even though the training (ground truth) data may not be entirely present from the...
Fast and accurate image classification is becoming one of the key requirements in content-based image retrieval (CBIR) system. The main idea of CBIR is to search similar image's based on user query. This paper proposes an improvised texture retrieval system using intuitionistic fuzzy set (IFS) theory. Tamura feature extraction technique is used to extract texture features of each image in the database...
This paper presents a new approach to formalizing the process of image storage and retrieval based on content considering the information extracted from the image through the segmentation phase and through the annotation phase. We constructed the Attributed Relational Graphs (ARGs) for describing the image contents from the independent domain. The structure of an ARG is made in accordance with a graph...
This paper proposes a new method to automatically index searches for relevant images using geo-coded information. Photographic images are labeled with their GPS (Global Positioning System) coordinates and date/time at the moment of capture and this date is then utilized to create two layer spatial and temporal indexes for image searches. A simulation performed to estimate the effectiveness of the...
Performing image preprocessing for specific targets, e.g., foreground (FG) segmentation and feature extraction, on the scale of databases is challenging. For volume image FG segmentation, we proposed to utilize dual multi-scale graylevel morphological open/close by reconstruction to simulate background (BG) gray-level variational mesh to identify FG regions. It is developed from a global perspective...
For content-based image retrieval, human emotions as well as object information provide important clue to search images. Accordingly, this paper presents a new retrieval system that indexes images using human emotion and searches them. Our system was tested with 1,300 textile images, then the results demonstrate the effectiveness of our system.
The content-based image retrieval (CBIR) is the most acceptable and often used image retrieval method, because it can be used to manage image database efficiently and effectively. The CBIR methods usually retrieve the images by image features. In this paper, we exploit a region called affine invariant region (AIR) as an image feature to help effectively retrieving the images which have been attacked...
an important problem in colour content-based image retrieval (CBIR) is the lack of an effective way to represent both the colour and spatial information of an image. In order to solve this problem, a new dominant colour descriptor that employs spatial information of image is proposed. A maximum of three dominant colour regions in an image together with their respective coordinates of the minimum-bounding...
Content-based image retrieval - CBIR uses visual content (low-level features) of images such as color, texture, shape, etc. to representand to index images. Extensive experiments on CBIR show that low-level features not represent exactly the high-level semantic concepts and can fail when used to retrieve similar images. In order to overpass this problem, different approaches aim to propose new methods...
Traditional image retrieval systems are content based image retrieval systems which rely on low-level features for indexing and retrieval of images. CBIR systems fail to meet user expectations because of the gap between the low level features used by such systems and the high level perception of images by humans. Semantics based methods have been used to describe images according to their high level...
Content-based retrieval of multimedia data has still been an active research area. The efficient retrieval in natural images has been proven a difficult task for content-based image retrieval systems. In this paper, we present a system that adapts two different index structures, namely Slim-Tree and BitMatrix, for efficient retrieval of images based on multidimensional low-level features such as color,...
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