Adaptive Content-Based Image Retrieval with Relevance Feedback

Cabarkapa, Slobodan; Kojić, Nenad; Radosavljević, Vladan; Zajic, Goran; Reljin, Branimir

doi:10.1109/eurcon.2005.1629880

Cited by 31 publications

(7 citation statements)

References 14 publications

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“…We decided to use maximal and minimal value of detail, and their positions, as characteristic features, as described in previous section. Precision =TP/(TP+FP) (8) The goal of experiment here is to assess whether it is possible to distinguish a performer. Observe that the song Something Stupid in the database is performed by two performers: Robbie Williams and Frank Sinatra.…”

Section: B Two-class Problemmentioning

confidence: 99%

Classification of performers using support vector machines

Reljin

Pokrajac

2008

2008 9th Symposium on Neural Network Applications in Electrical Engineering

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Huge amount of different music material in digital form, that can be found on the Internet, represents a big problem for a user who wants to find some particular music piece. Indexing, retrieving and classification are some of the techniques that can be used to provide faster search. In this paper, one of the methods for classification of the performers is described. We created audio databases, which consist of short audio sequences from several (nine) songs, sang by three distinct performers. Wavelet coefficients were used as feature descriptors for these music sequences. Classification was performed based on linear support vector machines. Our system exhibits very good results in the experiment with two performers (two classes): partial accuracies for classes were 100% and 98%, respectively. In the experiment with three classes (three performers), we used one-versus-one approach, and obtained partial accuracies of 50%, 82% and 35%, respectively.

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Section: B Two-class Problemmentioning

confidence: 99%

Classification of performers using support vector machines

Reljin

Pokrajac

2008

2008 9th Symposium on Neural Network Applications in Electrical Engineering

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“…Each class consists of 100 similar images, labeled by numerals, for instance: 0-99 (containing "Africans" -class 0), 100-199 (the "beach" scenes -class 1), 200-299 (monuments -class 2), 300-399 (busses -class 3), 400-499 (dinosaurs -class 4), 500-599 (elephants -class 5), 600-699 (flowers -class 6), 700-799 (horses -class 7), 800-899 (mountains -class 8), and 900-999 (cookies -class 9). Images in the database are described with low-level feature vectors, similarly to [10]. For each image the feature vector composing of low-level descriptors (color, texture, and shape) is created.…”

Section: A Datasetmentioning

confidence: 99%

“…In our previous work [10] an active learning strategy, exploiting both relevant and irrelevant images, is applied to update weights of query feature vector. Such a procedure combined with neural networks effectively brings out the most relevant images from the database.…”

Section: Introductionmentioning

confidence: 99%

The use of unlabeled data in image retrieval with relevance feedback

Radosavljević

Kojić²,

Zajic³

et al. 2008

2008 9th Symposium on Neural Network Applications in Electrical Engineering

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This paper describes a content-based image retrieval (CBIR) system which makes use of both labeled images, annotated by the user, and unlabeled images available in the database. The system initially retrieves images objectively closest to the query image. The user then subjectively labels retrieved images as relevant or irrelevant. Although such relevance feedback from the user is an effective way of bridging the semantic gap between objective and subjective similarity, it is also very time consuming, requiring huge human effort. Often, the number of labeled images is very small. In an inductive approach the labeled set of images is used for training a CBIR system while the large set of unlabeled images remains unused. In this paper we exploit the transductive Support Vector Machine (SVM) algorithm as a way of taking advantage of unlabeled data in CBIR. Our findings are compared to the results of an inductive SVM. We draw some conclusions as to when the use of unlabeled data might be helpful. The considered systems are tested over images from the CorellK dataset.

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“…Image i with the smallest distance d i is objectively the closest (i.e., more similar) to a query. After initial search, which is based on objective measure, the retrieving procedure may be improved by using user's relevance feedback [13,14,[18][19][20][21][22][23][24][25][26].…”

Section: Related Workmentioning

confidence: 99%

“…In this way, since the number of clusters K can be significantly lower than the number J of all feature vector components, the retrieving process will be accelerated accordingly. The rest of our system is of the structure that we already used in CBIR systems without feature vector reduction [25,26]. As a similarity measure, we used Mahalanobis distance while updating of the query feature vector is performed with assistance of radial basis function neural network.…”

Section: Feature Vector Reductionmentioning

confidence: 99%

Accelerating of Image Retrieval in CBIR System with Relevance Feedback

Zajic¹,

Kojić²,

Radosavljević

et al. 2007

EURASIP J. Adv. Signal Process.

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Recommended by Ebroul Izquierdo Content-based image retrieval (CBIR) system with relevance feedback, which uses the algorithm for feature-vector (FV) dimension reduction, is described. Feature-vector reduction (FVR) exploits the clustering of FV components for a given query. Clustering is based on the comparison of magnitudes of FV components of a query. Instead of all FV components describing color, line directions, and texture, only their representative members describing FV clusters are used for retrieval. In this way, the "curse of dimensionality" is bypassed since redundant components of a query FV are rejected. It was shown that about one tenth of total FV components (i.e., the reduction of 90%) is sufficient for retrieval, without significant degradation of accuracy. Consequently, the retrieving process is accelerated. Moreover, even better balancing between color and line/texture features is obtained. The efficiency of FVR CBIR system was tested over TRECVid 2006 and Corel 60 K datasets.

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Adaptive Content-Based Image Retrieval with Relevance Feedback

Cited by 31 publications

References 14 publications

Classification of performers using support vector machines

Classification of performers using support vector machines

The use of unlabeled data in image retrieval with relevance feedback

Accelerating of Image Retrieval in CBIR System with Relevance Feedback

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