Efficient query refinement for image retrieval

Nastar, Chahab; Mitschke, Matthias; Meilhac, Christophe

doi:10.1109/cvpr.1998.698659

Cited by 73 publications

(44 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the case of the simple temp oral model defined in Section V-A, this reduces to (32) In order to deal with a symmetric similarity measure, the similarity measure between elements and is defined by (33) Note that this similarity measure is not a metric since it does not satisfy the triangular inequality. However, it can be easily computed and interpreted, since it involves logarithms of likelihood ratios.…”

Section: A Statistical Similarity Measure Related To Motion Activitymentioning

confidence: 99%

Nonparametric motion characterization using causal probabilistic models for video indexing and retrieval

Fablet¹,

Bouthémy²,

Pérez³

2002

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-This paper describes an original approach for content-based video indexing and retrieval. We aim at providing a global interpretation of the dynamic content of video shots without any prior motion segmentation and without any use of dense optic flow fields. To this end, we exploit the spatio-temporal distribution, within a shot, of appropriate local motion-related measurements derived from the spatio-temporal derivatives of the intensity function. These distributions are then represented by causal Gibbs models. To be independent of camera movement, the motion-related measurements are computed in the image sequence generated by compensating the estimated dominant image motion in the original sequence. The statistical modeling framework considered makes the exact computation of the conditional likelihood of a video shot belonging to a given motion or more generally to an activity class feasible. This property allows us to develop a general statistical framework for video indexing and retrieval with query-by-example. We build a hierarchical structure of the processed video database according to motion content similarity. This results in a binary tree where each node is associated to an estimated causal Gibbs model. We consider a similarity measure inspired from Kullback-Leibler divergence. Then, retrieval with query-by-example is performed through this binary tree using the maximum a posteriori (MAP) criterion. We have obtained promising results on a set of various real image sequences.

show abstract

Section: A Statistical Similarity Measure Related To Motion Activitymentioning

confidence: 99%

Nonparametric motion characterization using causal probabilistic models for video indexing and retrieval

Fablet¹,

Bouthémy²,

Pérez³

2002

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…Some of the early efforts attempted to learn a new query and the relative importance of different features or feature components [27,19,20], while others attempted to learn a linear transformation in the feature space taking into account correlations among feature components [13,26], or to map the learning problem to a two-class discriminant classification problem [40]. In particular, inspired by term weighting and relevance feedback techniques in textual document retrieval, some of those efforts focused on heuristics-based techniques to adjust parameters empirically, such as [27,21].…”

Section: Related Workmentioning

confidence: 99%

BALAS: Empirical Bayesian learning in the relevance feedback for image retrieval

Zhang

2006

Image and Vision Computing

View full text Add to dashboard Cite

This paper is on user relevance feedback in image retrieval. We take this problem as a standard two-class pattern classification problem aiming at refining the retrieval precision by learning through the user relevance feedback data. However, we have investigated the problem by noting two important unique characteristics of the problem: small sample collection and asymmetric sample distributions between positive and negative samples. We have developed a novel approach to empirical Bayesian learning to solve for this problem by explicitly exploiting the two unique characteristics, which is the methodology of BAyesian Learning in Asymmetric and Small sample collections, thus called BALAS. In BALAS different learning strategies are used for positive and negative sample collections, respectively, based on the two unique characteristics. By defining the relevancy confidence as the relevant posterior probability, we have developed an integrated ranking scheme in BALAS which complementarily combines the subjective relevancy confidence and the objective similarity measure to capture the overall retrieval semantics. The experimental evaluations have confirmed the rationale of the proposed ranking scheme, and have also demonstrated that BALAS is superior to an existing relevance feedback method in the current literature in capturing the overall retrieval semantics.

show abstract

“…In order to make the estimation feasible during query-time, simplifying assumptions have to be made. In Nastar et al (1998), the feature components are assumed to be independent and their distribution is assumed to be unimodal and Gaussian. The images to be shown on the next round are determined using modified maximum likelihood estimation which takes into account also the set of non-relevant seen images D − (n).…”

Section: Density Estimationmentioning

confidence: 99%

“…Feature combination can also be performed with other approaches such as voting and Borda count. For example, a voting procedure is presented in Nastar et al (1998), where each feature is used to grade images separately and the final ranks of retrieved images are obtained by averaging the separate ratings. A modified Borda count method was used to combine results from multiple features in Jeong et al (1999).…”

Section: Feature Selection and Synthesismentioning

confidence: 99%

Content-Based Image Retrieval Using Self-Organizing Maps

Laaksonen

Koskela

Oja

1999

Visual Information and Information Systems

View full text Add to dashboard Cite

Digital image libraries are becoming more common and widely used as visual information is produced at a rapidly growing rate. Creating and storing digital images is nowadays easy and getting more affordable all the time as the needed technologies are maturing and becoming eligible for general use. As a result, the amount of data in visual form is increasing and there is a strong need for effective ways to manage and process it. In many settings, the existing and widely adopted methods for text-based indexing and information retrieval are inadequate for these new purposes.Content-based image retrieval addresses the problem of finding images relevant to the users' information needs from image databases, based principally on low-level visual features for which automatic extraction methods are available. Due to the inherently weak connection between the high-level semantic concepts that humans naturally associate with images and the low-level visual features that the computer is relying upon, the task of developing this kind of systems is very challenging. A popular method to improve retrieval performance is to shift from single-round queries to navigational queries where a single retrieval instance consists of multiple rounds of user-system interaction and query reformulation. This kind of operation is commonly referred to as relevance feedback and can be considered as supervised learning to adjust the subsequent retrieval process by using information gathered from the user's feedback.In this thesis, an image retrieval system named PicSOM is presented, including detailed descriptions of using multiple parallel Self-Organizing Maps (SOMs) for image indexing and a novel relevance feedback technique. The proposed relevance feedback technique is based on spreading the user responses to local SOM neighborhoods by a convolution with a kernel function. A broad set of evaluations with different image features, retrieval tasks, and parameter settings demonstrating the validity of the retrieval method is described. In particular, the results establish that relevance feedback with the proposed method is able to adapt to different retrieval tasks and scenarios.Furthermore, a method for using the relevance assessments of previous retrieval sessions or potentially available keyword annotations as sources of semantic information is presented. With performed experiments, it is confirmed that the efficiency of semantic image retrieval can be substantially increased by using these features in parallel with the standard low-level visual features.

show abstract

Efficient query refinement for image retrieval

Cited by 73 publications

References 9 publications

Nonparametric motion characterization using causal probabilistic models for video indexing and retrieval

Nonparametric motion characterization using causal probabilistic models for video indexing and retrieval

BALAS: Empirical Bayesian learning in the relevance feedback for image retrieval

Content-Based Image Retrieval Using Self-Organizing Maps

Contact Info

Product

Resources

About