@InProceedings{6952413,
  author = {E. Giouvanakis and C. Kotropoulos},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Boosting the weights of positive words in image retrieval},
  year = {2014},
  pages = {1168-1172},
  abstract = {In this paper, an image retrieval system based on the bag-of-words model is developed, which contains a novel query expansion technique. SIFT image features are computed using the Hessian-Affine keypoint detector. All feature descriptors are taken into account for the bag-of-words representation by dividing the full set of descriptors into a number of subsets. For each subset, a partial vocabulary is created and the final vocabulary is obtained by the union of the partial vocabularies. Here, a new discriminative query expansion technique is proposed in which an SVM classifier is trained in order to obtain a decision boundary between the top ranked and the bottom ranked images. Treating this boundary as a new query, words appearing exclusively in top-ranked images are further boosted by rewarding them with larger weights. The images are re-ranked with respect to the their distance from the new boosted query. It is proved that this strategy improves image retrieval performance.},
  keywords = {image classification;image representation;image retrieval;support vector machines;transforms;positive words;image retrieval system;novel discriminative query expansion technique;SIFT image features;Hessian-Affine keypoint detector;bag-of-words representation model;partial vocabulary;SVM classifier;top-ranked images;Visualization;Vocabulary;Vectors;Computer vision;Image retrieval;Feature extraction;Support vector machines;image retrieval;bag-of-words;query-expansion},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569923347.pdf},
}

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For each subset, a partial vocabulary is created and the final vocabulary is obtained by the union of the partial vocabularies. Here, a new discriminative query expansion technique is proposed in which an SVM classifier is trained in order to obtain a decision boundary between the top ranked and the bottom ranked images. Treating this boundary as a new query, words appearing exclusively in top-ranked images are further boosted by rewarding them with larger weights. The images are re-ranked with respect to the their distance from the new boosted query. It is proved that this strategy improves image retrieval performance.","keywords":"image classification;image representation;image retrieval;support vector machines;transforms;positive words;image retrieval system;novel discriminative query expansion technique;SIFT image features;Hessian-Affine keypoint detector;bag-of-words representation model;partial vocabulary;SVM classifier;top-ranked images;Visualization;Vocabulary;Vectors;Computer vision;Image retrieval;Feature extraction;Support vector machines;image retrieval;bag-of-words;query-expansion","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569923347.pdf","bibtex":"@InProceedings{6952413,\n author = {E. Giouvanakis and C. Kotropoulos},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Boosting the weights of positive words in image retrieval},\n year = {2014},\n pages = {1168-1172},\n abstract = {In this paper, an image retrieval system based on the bag-of-words model is developed, which contains a novel query expansion technique. SIFT image features are computed using the Hessian-Affine keypoint detector. All feature descriptors are taken into account for the bag-of-words representation by dividing the full set of descriptors into a number of subsets. For each subset, a partial vocabulary is created and the final vocabulary is obtained by the union of the partial vocabularies. Here, a new discriminative query expansion technique is proposed in which an SVM classifier is trained in order to obtain a decision boundary between the top ranked and the bottom ranked images. Treating this boundary as a new query, words appearing exclusively in top-ranked images are further boosted by rewarding them with larger weights. The images are re-ranked with respect to the their distance from the new boosted query. 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