Content-based image retrieval

About: Content-based image retrieval is a research topic. Over the lifetime, 6916 publications have been published within this topic receiving 150696 citations. The topic is also known as: CBIR.

Content-based image retrieval with color and texture features in neutrosophic domain

Abstract: In this paper, a new content-based image retrieval (CBIR) scheme is proposed in neutrosophic (NS) domain. For this task, RGB images are first transformed to three subsets in NS domain and then segmented. For each segment of an image, color features including dominant color discribtor (DCD), histogram and statistic components are extracted. Wavelet features are also extracted as texture features from the whole image. All extracted features from either segmented image or the whole image are combined to create a feature vector. Feature vectors are presented for ant colony optimization (ACO) feature selection which selects the most relevant features. Selected features are used for final retrieval process. Proposed CBIR scheme is evaluated on Corel image dataset. Experimental results show that the proposed method outperforms our prior method (with the same feature vector and feature selection method) by 2% and 1% with respect to precision and recall, respectively. Also, the proposed method achieves the improvement of 13% and 2% in precision and recall, respectively, in comparison with prior methods.

In-Plane Rotation and Scale Invariant Clustering Using Dictionaries

Abstract: In this paper, we present an approach that simultaneously clusters images and learns dictionaries from the clusters. The method learns dictionaries and clusters images in the radon transform domain. The main feature of the proposed approach is that it provides both in-plane rotation and scale invariant clustering, which is useful in numerous applications, including content-based image retrieval (CBIR). We demonstrate the effectiveness of our rotation and scale invariant clustering method on a series of CBIR experiments. Experiments are performed on the Smithsonian isolated leaf, Kimia shape, and Brodatz texture datasets. Our method provides both good retrieval performance and greater robustness compared to standard Gabor-based and three state-of-the-art shape-based methods that have similar objectives.

Content-based image retrieval using wavelet-based salient points

Abstract: Content-based Image Retrieval (CBIR) has become one of the most active research areas in the past few years. Most of the attention from the research has been focused on indexing techniques based on global feature distributions. However, these global distributions have limited discriminating power because they are unable to capture local image information. Applying global Gabor texture features greatly improve the retrieval accuracy. But they are computationally complex. In this paper, we present a wavelet-based salient point extraction algorithm. We show that extracting the color and texture information in the locations given by these points provides significantly improved results in terms of retrieval accuracy, computational complexity and storage space of feature vectors as compared to the global feature approaches.

LIRE: open source image retrieval in Java

Abstract: Content based image retrieval has been around for some time. There are lots of different test data sets, lots of published methods and techniques, and manifold retrieval challenges, where content based image retrieval is of interest. LIRE is a Java library, that provides a simple way to index and retrieve millions of images based on the images' contents. LIRE is robust and well tested and is not only recommended by the websites of ImageCLEF and MediaEval, but is also employed in industry. This paper gives an overview on LIRE, its use, capabilities and reports on retrieval and runtime performance.

Navigation and Browsing of Image Databases

Abstract: Image collections are growing at an exponential rate and solutions to manage vast databases of images are hence highly sought after. Content-based image retrieval techniques have shown great potential, yet commonly employed approaches like query-by-example are only of limited usefulness. An interesting alternative is provided by systems that allow visual exploration of an image dataset through a browsing interface. In these methods the complete database, or parts thereof, is visualised through application of dimensionality reduction techniques, clustered visualisations or display of a graph structure. Once visualised, it should then be possible to browse through the collection in an interactive, intuitive and efficient way. In this paper we present various browsing techniques that can be employed for this purpose. Browsing can be achieved in several ways. We can distinguish between horizontal browsing which works on images of the visualisation plane, and includes operations such as panning, zooming, magnification and scaling, and vertical browsing which allows navigation to a different level of a hierarchically organised visualisation. Furthermore, browsing can also be accomplished by taking into account time stamp information, hence enabling temporal browsing. We conclude, highlighting the need for objective evaluation and benchmarking of browsing system and see one of the next research challenges in the development of effective image browsing tools for mobile devices.