Faouzi Alaya Cheikh

A weighted distance approach to relevance feedback

Content-based image retrieval systems use low-level features like color and texture for image representation. Given these representations as feature vectors, similarity between images is measured by computing distances in the feature space. Unfortunately, these low-level features cannot always capture the high-level concept of similarity in human perception. Relevance feedback tries to improve the performance by allowing iterative retrievals where the feedback information from the user is incorporated into the database search. We present a weighted distance approach where the weights are the ratios of standard deviations of the feature values both for the whole database and also among the images selected as relevant by the user. The feedback is used for both independent and incremental updating of the weights and these weights are used to iteratively refine the effects of different features in the database search. Retrieval performance is evaluated using average precision and progress that are computed on a database of approximately 10,000 images and an average performance improvement of 19% is obtained after the first iteration.

High-resolution digital resampling using vector rational filters

Rational filters are extended to multichannel signal process- ing and applied to image interpolation. Two commonly used decimation schemes are considered: a rectangular grid and a quincunx grid. For each decimation lattice, we propose a number of adaptive resampling algorithms based on the vector rational filter (VRF). These algorithms exhibit desirable properties such as edge and detail preservation and accurate chromaticity estimation. In these approaches, color image pix- els are considered as three-component vectors in the color space. Therefore, the inherent correlation that exists between the different color components is not ignored. This leads to better image quality compared to that obtained by componentwise or marginal processing. Extensive simulations show that multichannel image processing with the proposed algorithms (VRFL) and (VRFd) based on l p-norm and directional pro- cessing, respectively; significantly outperform linear and some nonlinear techniques, e.g., vector FIR median hybrid filters (VFMH). Some images interpolated using VRFL and VRFd are presented for qualitative compari- son. These images are free from blockiness and jaggedness, confirming the quantitative results.

Directional-rational approach for color image enhancement

In this paper, we present an unsharp masking-based approach for noise smoothing and edge enhancing in multichannel images. The proposed structure is similar to the conventional unsharp masking structure, however, the enhancement is allowed only in the direction of maximal change and the enhancement parameter is computed as a nonlinear function of the rate of change. The proposed scheme enhances the true details, limits the overshoot near sharp edges and attenuates noise in flat areas. Moreover the use of the control function eliminates the need for the subjective coefficient /spl lambda/ used in the conventional unsharp masking technique. Simulations results show that the processed image presents sharp edges which makes it more pleasant to the human eye. Moreover, the amount of noise in the image is clearly reduced.

Color image interpolation using vector rational filters

Rational filters are extended to multichannel signal processing and applied to the image interpolation problem. The proposed nonlinear interpolator exhibits desirable properties, such as, edge and details preservation. In this approach the pixels of the color image are considered as 3-component vectors in the color space. Therefore, the inherent correlation which exists between the different color components is not ignored; thus, leading to better image quality than those obtained by component-wise processing. Simulations show that the resulting edges obtained using vector rational filters (VRF) are free from blockiness and jaggedness, which are usually present in images interpolated using especially linear, but also some nonlinear techniques, e.g. vector median hybrid filters (VFMH).

Ordinal-measure based shape correspondence

We present a novel approach to shape similarity estimation based on distance transformation and ordinal correlation. The proposed method operates in three steps: object alignment, contour to multilevel image transformation, and similarity evaluation. This approach is suitable for use in shape classification, content-based image retrieval and performance evaluation of segmentation algorithms. The two latter applications are addressed in this papers. Simulation results show that in both applications our proposed measure performs quite well in quantifying shape similarity. The scores obtained using this technique reflect well the correspondence between object contours as humans perceive it.

Content-based object retrieval using maximum curvature points in contour images

In this paper we present a technique for shape similarity estimation for content-based indexing and retrieval over large image databases. Here the high curvature points are detected using wavelet decomposition. The feature set is extracted under the framework of polygonal approximation. It uses simple features extracted at high curvature points. The experimental result and comparisons show the performance of the proposed technique. This technique is also suitable to be extended to the retrieval of 3D objects.

Multi-level shape recognition based on wavelet-transform modulus maxima

In this paper we propose a new approach to shape recognition based on the wavelet transform modulus maxima, and we apply it to the problem of content-based indexing and retrieval of fish contours. The description scheme and the similarity measure developed take into consideration the way our visual system perceives objects and compares them. The proposed scheme is invariant to translation, rotation, scale change and to noise corruption. Moreover, this description scheme allows accurate reconstruction of the shape boundary from the feature vector used to describe it. The experimental results and comparisons show the performance of the proposed technique.

Relevance feedback for shape query refinement

In this paper we propose to incorporate a feedback loop, into the ordinal correlation framework and apply it to shape-based image retrieval. The users feedback on the relevance of the retrieval results is used to tune the weights of the similarity measure. Statistics from the features of both relevant and irrelevant items are used to estimate the weights. Moreover, the information accumulated from previous retrieval iterations is used in the weights estimation. A simple measure of the discrimination power is proposed and used to show that the relevance feedback increases the capability of the ordinal correlation scheme to discriminate between relevant and irrelevant objects.

Directional Unsharp Masking-Based Approach for Color Image Enhancement

In this paper, we present an unsharp masking-based approach for noise smoothing and edge enhancing in multichannel images. The proposed structure is similar to the conventional unsharp masking structure, however, the enhancement is allowed only in the direction of maximal change and the enhancement parameter is computed as a nonlinear function of the rate of change. The proposed scheme enhances the true details, limits the overshoot near sharp edges and attenuates noise in flat areas. Moreover the use of the control function eliminates the need for the subjective coefficient λ used in the conventional unsharp masking technique.

Audio-based queries for video retrieval over Java enabled mobile devices

In this paper we propose a generic framework for efficient retrieval of audiovisual media based on its audio content. This framework is implemented in a client-server architecture where the client application is developed in Java to be platform independent whereas the server application is implemented for the PC platform. The client application adapts to the characteristics of the mobile device where it runs such as screen size and commands. The entire framework is designed to take advantage of the high-level segmentation and classification of audio content to improve speed and accuracy of audio-based media retrieval. Therefore, the primary objective of this framework is to provide an adaptive basis for performing efficient video retrieval operations based on the audio content and types (i.e. speech, music, fuzzy and silence). Experimental results approve that such an audio based video retrieval scheme can be used from mobile devices to search and retrieve video clips efficiently over wireless networks.