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 using multiresolution histogram representation

Abstract: We present two new approaches based on color histogram indexing for content-based retrieval of image databases. Since the high computational complexity has been one of the main barriers towards the use of similarity measures such as histogram intersection in large databases, we propose a hierarchical indexing scheme where computationally efficient features are used to subset the image before more sophisticated techniques are applied for precise retrieval. The use of histograms at different color resolutions as filtering and matching features in a hierarchical scheme is studied. In the second approach, a multiresolution representation of the histogram using the indices and signs of its largest wavelet coefficients is examined. Excellent results have been observed using the latter method.

Multilabel Neighborhood Propagation for Region-Based Image Retrieval

Abstract: Content-based image retrieval (CBIR) has been an active research topic in the last decade. As one of the promising approaches, graph-based semi-supervised learning has attracted many researchers. However, while the related work mainly focused on global visual features, little attention has been paid to region-based image retrieval (RBIR). In this paper, a framework based on multilabel neighborhood propagation is proposed for RBIR, which can be characterized by three key properties: (1) For graph construction, in order to determine the edge weights robustly and automatically, mixture distribution is introduced into the Earth mover's distance (EMD) and a linear programming framework is involved. (2) Multiple low-level labels for each image can be obtained based on a generative model, and the correlations among different labels are explored when the labels are propagated simultaneously on the weighted graph. (3) By introducing multilayer semantic representation (MSR) and support vector machine (SVM) into the long-term learning, more exact weighted graph for label propagation and more meaningful high-level labels to describe the images can be calculated. Experimental results, including comparisons with the state-of-the-art retrieval systems, demonstrate the effectiveness of our proposal.

Psychophysical studies of the performance of an image database retrieval system

Abstract: We describe psychophysical experiments conducted to study PicHunter, a content-based image retrieval (CBIR) system. Experiment 1 studies the importance of using (a) semantic information, (2) memory of earlier input and (3) relative, rather than absolute, judgements of image similarity. The target testing paradigm is used in which a user must search for an image identical to a target. We find that the best performance comes from a version of PicHunter that uses only semantic cues, with memory and relative similarity judgements. Second best is use of both pictorial and semantic cues, with memory and relative similarity judgements. Most reports of CBIR systems provide only qualitative measures of performance based on how similar retrieved images are to a target. Experiment 2 puts PicHunter into this context with a more rigorous test. We first establish a baseline for our database by measuring the time required to find an image that is similar to a target when the images are presented in random order. Although PicHunter's performance is measurably better than this, the test is weak because even random presentation of images yields reasonably short search times. This casts doubt on the strength of results given in other reports where no baseline is established.

An Interactive Semantic Video Mining and Retrieval Platform--Application in Transportation Surveillance Video for Incident Detection

Abstract: Understanding and retrieving videos based on their semantic contents is an important research topic in multimedia data mining and has found various real- world applications. Most existing video analysis techniques focus on the low level visual features of video data. However, there is a "semantic gap" between the machine-readable features and the high level human concepts i.e. human understanding of the video content. In this paper, an interactive platform for semantic video mining and retrieval is proposed using relevance feedback (RF), a popular technique in the area of content-based image retrieval (CBIR). By tracking semantic objects in a video and then modeling spatio-temporal events based on object trajectories and object interactions, the proposed interactive learning algorithm in the platform is able to mine the spatio-temporal data extracted from the video. An iterative learning process is involved in the proposed platform, which is guided by the user's response to the retrieved results. Although the proposed video retrieval platform is intended for general use and can be tailored to many applications, we focus on its application in traffic surveillance video database retrieval to demonstrate the design details. The effectiveness of the algorithm is demonstrated by our experiments on real-life traffic surveillance videos.