Image Information Systems: Where Do We Go From Here?

Chang, Shi-Kuo; Hsu, Arding

doi:10.1142/9789814343138_0035

Cited by 29 publications

(45 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, in current video database systems only rudimentary techniques such as keyword-based searches and fast forward/backward browsing are provided. Current keyword-based methods and video browsing techniques have many drawbacks [1], such as inadequate choice of keywords to use in a query, the context in which the words are used, and the influence of the operator during the construction of the database and more importantly at search time. Therefore, development of more advanced searching and browsing methods based on video content has become more and more important for future video database systems.…”

Section: Introductionmentioning

confidence: 99%

<title>Feature management for large video databases</title>

1993

Self Cite

View full text Add to dashboard Cite

Large video databases, as well as many other applications involving video, such as multimedia, training, and "movie-on-demand" systems, require efficient steps to manipulate the enormous amount of data associated with full motion video. In this paper the incoming video is systematically and efficiently reduced via a frame selection procedure which takes advantage of the fact that the incoming video is encoded using one of several existing DCT-based standards. The procedure is performed in the frequency domain prior to video decoding. Further refinement in the frame selection step is achieved using a robust metric based upon the color histogram of the selected subset of decoded frames. The procedure is presented in detail and several examples are exhibited

show abstract

Section: Introductionmentioning

confidence: 99%

<title>Feature management for large video databases</title>

1993

Self Cite

View full text Add to dashboard Cite

show abstract

“…The earliest image retrieval technique is the text-based image retrieval. This method annotates images by text and then used database technique to perform image retrieval [1]. Because it is impossible to annotate images in data stream, this method is obviously not appropriate to data stream image retrieval.…”

Section: Introductionmentioning

confidence: 99%

Image retrieval in data stream using principle component analysis

Yan

Liu

2012

2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet)

View full text Add to dashboard Cite

Image Retrieval in Data Stream is becoming very important in recent years with the development of internet and the focus on information security. In Data Stream, image retrieval is different from the traditional image retrieval in that, it requires high efficiency and network adaptive ability, while the traditional image retrieval does not have such hard requirements. In this paper, we propose a novel approach to retrieval image in data stream using Principle Component Analysis(PCA). By subdividing image into several blocks and extracting image principle features, our method can retrieval image very efficiently, this meets the needs of wide band width network traffic. Because the natural character of PCA, our method can be adaptive to the network traffic by simply modify a few parameters by an automatic way. Our experiments show that our approach has high Recall Rate and very low False Positive Rate.

show abstract

“…The first important step in every image retrieval system is feature representation. It is to find a way in describing the content of every image in the database [24], [3], [13], [20]. S Simple colour histogram is one of the methods to index each image.…”

Section: Introductionmentioning

confidence: 99%

Combined feature descriptor for Content based Image Retrieval

Selvarajah

Kodithuwakku

2011

2011 6th International Conference on Industrial and Information Systems

View full text Add to dashboard Cite

Content based Image Retrieval (CBIR) allows automatically extracting target images according to objective visual contents of the image itself. Representation of visual features and similarity match are important issues in CBIR. Colour and texture features are important properties in CBIR systems. In this paper, a combined feature descriptor for CBIR is proposed to enhance the retrieval performance for CBIR. This method is developed by exploiting the wavelets and colour histogram moments. First, Haar wavelet is used to decompose colour images into wavelet coefficients. Then image feature extraction and similarity matching are performed by means of histogram moments. Ten categories of colour images are used to test the proposed technique. Experiment results show significant improvement of retrieval efficiency as compared to that of histogram moments as well as 2D Discrete Wavelet Transform.

show abstract

Image Information Systems: Where Do We Go From Here?

Cited by 29 publications

References 0 publications

<title>Feature management for large video databases</title>

<title>Feature management for large video databases</title>

Image retrieval in data stream using principle component analysis

Combined feature descriptor for Content based Image Retrieval

Contact Info

Product

Resources

About