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.

Application of Image SIFT Features to the Context of CBIR

Abstract: This paper is mainly concerned with the application of a kind of distinctive local invariant feature i.e. Lowe's SIFT feature for the purpose of CBIR, instead of the usually used global feature and local statistical feature based on image segmentation. In our CBIR system, the visual contents of the query image and the database images are extracted and described by the 128-dimensional SIFT feature vectors. The KD-tree with the best bin first(BBF), an approximate nearest neighbors(ANN) search algorithm, is used to index and match those SIFT features. As our contribution, a modified voting scheme called nearest neighbor distance ratio scoring (NNDRS) is put forward to calculate the aggregate scores of the corresponding candidate images in the database respectively. By sorting the database images according to their aggregate scores in descending order, the top few similar images are shown to users as the retrieval results. Additionally, RANSAC can be adopted as a geometry verification method to re-check the results and remove the false matches. Experiments show that our approach can obtain high recall and high precision in the context of CBIR on the famous image databases ZuBud.

Assessing the best integration between distance-function and image-feature to answer similarity queries

Abstract: The retrieval of multimedia data relies on a feature extractor to provide the intrinsic characteristics (features) from the data, and a measure to quantify the similarity between them. A challenge in multimedia database systems is how to best integrate these two key aspects in order to improve the quality of the retrieved selection when answering similarity queries. In this paper, we analyze and compare a set of distance functions and feature extractors with regard to the association and dependencies among them. The results show that the most widely used and well-known distance functions, such as the Euclidean distance, do not reach a desirable similarity assessment, and reveal that a careful choice of a distance function considerably improves the retrieval of multimedia data, which in our experiments reached up to 92%.

Binary SIFT: Fast image retrieval using binary quantized SIFT features

Abstract: SIFT features are widely used in content based image retrieval. Typically, a few thousand keypoints are extracted from each image. Image matching involves distance computations across all pairs of SIFT feature vectors from both images, which is quite costly. We show that SIFT features perform surprisingly well even after quantizing each component to binary, when the medians are used as the quantization thresholds. Quantized features preserve both distinctiveness and matching properties. Almost all of the features in our 5.4 million feature test set map to distinct binary patterns after quantization. Furthermore, number of matches between images using both the original and the binary quantized SIFT features are quite similar. We investigate the distribution of SIFT features and observe that the space of 128-D binary vectors has sufficient capacity for the current performance of SIFT features. We use component median values as quantization thresholds and show through vector-to-vector distance comparisons and image-to-image matches that the resulting binary vectors perform comparable to original SIFT vectors. We also discuss computational and storage gains. Binary vector distance computation reduces to bit-wise operations. Square operation is eliminated. Fast and efficient indexing techniques such as the signatures used for chemical databases can also be considered.

An Effective Content-Based Image Retrieval Using Color, Texture and Shape Feature

Abstract: Content based image retrieval is an active area of research for more than a decade. In this paper, we have proposed an effective way of extracting color, texture, and shape features from image and combine them in a way that ensures higher retrieval efficiency. For extraction of color features, images are divided into non-overlapping blocks, and dominant color of each block is determined using k-means algorithm. For extracting gray-level co-occurrence matrix (GLCM)-based texture features, each pixel in the image is replaced by average value of its neighborhood pixels. These average values are further quantized into 16 levels, for better and efficient representation of texture in the database. Finally, Fourier descriptors are extracted from the segmented image and are used to represent the shape of objects, as they have better representation capability and robust to noise, than other shape descriptors. The feature vector formed by combining all these is used to represent image in the database. We have tested our approach on wang dataset. Experimental results show that the present scheme has achieved higher retrieval accuracy on representative color image databases.

3D Lung Image Retrieval Using Localized Features

Abstract: The interpretation of high-resolution computed tomography (HRCT) images of the chest showing disorders of the lung tissue associated with interstitial lung diseases (ILDs) is time-consuming and requires experience. Whereas automatic detection and quantification of the lung tissue patterns showed promising results in several studies, its aid for the clinicians is limited to the challenge of image interpretation, letting the radiologists with the problem of the final histological diagnosis. Complementary to lung tissue categorization, providing visually similar cases using content-based image retrieval (CBIR) is in line with the clinical workflow of the radiologists. In a preliminary study, a Euclidean distance based on volume percentages of five lung tissue types was used as inter-case distance for CBIR. The latter showed the feasibility of retrieving similar histological diagnoses of ILD based on visual content, although no localization information was used for CBIR. However, to retrieve and show similar images with pathology appearing at a particular lung position was not possible. In this work, a 3D localization system based on lung anatomy is used to localize low-level features used for CBIR. When compared to our previous study, the introduction of localization features allows improving early precision for some histological diagnoses, especially when the region of appearance of lung tissue disorders is important.