Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.

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Content-based image retrieval at the end of the early years

Image Analysis and Computer Vision: 1998☆

This paper proposes a reliable method for tracking the trajectory of video objects using the vector Kalman predictor. Video objects, within the scope of this paper, are defined as groups of image pixels coherent spatially as well as in their values of luminance. The extent to which the quality of the unsupervised region split and merge segmentation affects the accuracy of the tracker is discussed, alongside the improvements made in the segmentation process as a result of the feedback from the tracking algorithm. The overall low complexity of the system, and the time savings made in using a spiral search algorithm, provide this method with prospects of being implemented in real-time.

/pdf/video-object-tracking-using-region-split-and-merge-and-a-bns48z5i6i.pdf

Video object tracking using region split and merge and a Kalman filter tracking algorithm

The 2D string approaches provide a natural way of constructing iconic indexing for images. The 2D C-string representation with an efficient cutting mechanism is more characteristic of spatial knowledge and efficient in the representation of iconic images. However, the computation of object ranks in a 2D C-string might make the inference of spatial reasoning somewhat complicated. This shortcoming is overcome by the 2D C-tree representation. The 2D C-tree not only keeps the comprehensive spatial knowledge in the original 2D C-string, but also the ordered labeled tree is more suitable for spatial reasoning and image retrieval. The spatial knowledge can be derived directly from the inference rules embedded in the characteristic structure of the 2D C-tree representation.

https://ir.nctu.edu.tw:443/bitstream/11536/31430/1/000080088800003.pdf

2D C-tree spatial representation for iconic image

Video data contains a large amount of spatial and temporal information. The changes of video frames are quite useful for motion analysis and cannot be provided by other media easily. Chang et al. had proposed an effective 2D string approach for spatial indexing of image data. In this paper, we extend this iconic approach and apply it to video data indexing. We propose 2D C-trees to represent the spatial content within individual frames. A video sequence can then be represented and indexed by a temporal set or an ordering set of 2D C-trees. The similarity retrieval of video matching problem becomes the problem of video sequence matching by computing the similarity, or the minimum cost of matched frames. We present three schemes, full-sequence matching, segment matching and subsequence matching, for video information retrieval. The matching schemes can be modified and extended to approximate sequence matching by computing the partial distance for providing a comprehensive retrieval of video data. A prototype video information system is also developed to validate the effectiveness of video data indexing by 2D C-trees. ( 1998 Academic Press

https://ir.nctu.edu.tw:443/bitstream/11536/32458/1/000075623500002.pdf

Video Data Indexing by 2D C-Trees

https://ir.nctu.edu.tw:443/bitstream/11536/32736/1/000074615800009.pdf

Bao-Shuh Lin

Papers

Similarity Retrieval by 2D C-Trees Matching in Image Databases