Fast wavelet histogram techniques for image indexing

Mandal, Mrinal; Panchanathan, S.

doi:10.1109/ivl.1998.694498

Cited by 20 publications

(23 citation statements)

References 6 publications

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“…This approach is computationally expensive and it requires a huge amount of memory storage. Another method introduced to reduce the computational complexity of colour histogram in terms of its moments [15]. Discrete image transforms have been widely studied and suggested for many image retrieval applications.…”

Section: Previous Workmentioning

confidence: 99%

Combined feature descriptor for Content based Image Retrieval

Selvarajah

Kodithuwakku

2011

2011 6th International Conference on Industrial and Information Systems

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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.

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Section: Previous Workmentioning

confidence: 99%

Combined feature descriptor for Content based Image Retrieval

Selvarajah

Kodithuwakku

2011

2011 6th International Conference on Industrial and Information Systems

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“…To reduce complexity and improve the robustness to illumination changes, [13] proposes to model the histograms using a generalized Gaussian density function. Other indexing techniques are texture-oriented [14].…”

Section: Refinementmentioning

confidence: 99%

Scalable indexing of HD Video

Morand

Benois-Pineau

Domenger

2008

2008 International Workshop on Content-Based Multimedia Indexing

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Video indexing technique is crucial in multimedia applications. In the case of HD (High Definition) Video, the principle of scalability is of great importance. The wavelet decomposition used in the MJPEG2000 provides this property. In this paper, two descriptors for indexing of HD Video are presented. The first one is based on the GM (Global Motion) of the video and the correlation matrix is used as similarity measure for matching. The second one is object-oriented and uses histograms in the wavelet domain as indexes ; similarity measure is then based on histogram intersection. The complete process of extraction of objects of interest and to determine GM are also detailed.

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“…In this study, the Haar wavelets are used as features for similarity analysis [32,33]. The main reasons for the selection of the Haar wavelets include their relative simplicity, orthogonality, and ease of normalization.…”

Section: Waveletsmentioning

confidence: 99%

Similarity Analysis of Video Sequences Using an Artificial Neural Network

Bhandarkar

Chen

2005

Appl Intell

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Comparison of video sequences is an important operation in many multimedia information systems. The similarity measure for comparison is typically based on some measure of correlation with the perceptual similarity (or difference) amongst the video sequences or with the similarity (or difference) in some measure of semantics associated with the video sequences. In content-based similarity analysis, the video data are expressed in terms of different features. Similarity matching is then performed by quantifying the feature relationships between the target video and query video shots, with either an individual feature or with a feature combination. In this study, two approaches are proposed for the similarity analysis of video shots. In the first approach, mosaic images are created from video shots, and the similarity analysis is done by determining the similarities amongst the mosaic images. In the second approach, key frames are extracted for each video shot and the similarity amongst video shots is determined by comparing the key frames of the video shots. The features extracted include image histograms, slopes, edges, and wavelets. Both individual features and feature combinations are used in similarity matching using an artificial neural network. The similarity rank of the query video shots is determined based on the values of the coefficients of determination and the mean absolute error. The study reported in this paper shows that the mosaicbased similarity analysis can be expected to yield a more reliable result, whereas the key frame-based similarity analysis could be potentially applied to a wider range of applications. The weighted non-linear feature combination is shown to yield better results than a single feature for video similarity analysis. The coefficient of determination is shown to be a better criterion than the mean absolute error in similarity matching analysis.

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Fast wavelet histogram techniques for image indexing

Cited by 20 publications

References 6 publications

Combined feature descriptor for Content based Image Retrieval

Combined feature descriptor for Content based Image Retrieval

Scalable indexing of HD Video

Similarity Analysis of Video Sequences Using an Artificial Neural Network

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