Abstract
The KNN classification algorithm is particularly suited to be used when classifying images described by local features. In this paper, we propose a novel image classification approach, based on local descriptors and the KNN algorithm. The proposed scheme is based on a hierarchical categorization tree that uses both supervised and unsupervised classification techniques. The unsupervised one is based on a hierarchical lattice vector quantization algorithm, while the supervised one is based on both feature vectors labelling and supervised feature selection method. The proposed tree improves the effectiveness of local feature vector classification and outperforms the exact KNN algorithm in terms of categorization accuracy.
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References
Amar CB, Zaied M, Alimi MA (2005) Beta wavelets. synthesis and application to lossy image compression. Adv Eng Softw 36(7):459–474
Athitsos V, Sclaroff S (2005) Boosting nearest neighbor classifiers for multiclass recognition. In: CVPR ‘05, IEEE Computer Society, Washington, DC
Bellil W, Amar CB, Alimi MA (2003) Beta wavelet based image compression. In: International conference on signal system and design SSD03, pp 77–82
Bouteldja N, Gouet-Brunet V, Scholl M (2006) Evaluation of strategies for multiple sphere queries with local image descriptors. In: IST/SPIE conference on multimedia content analysis, management, and retrieval. San Jose, CA
Claveau V, Tirilly P, Gros P (2008) Language modeling for bag-of visual words image categorization. In: CIVR ’08: proceedings of the 2008 international conference on content-based image and video retrieval, pp 249–258
Everingham M, Gool LV, Williams CKI, JohnWinn, Zisserman, A (2009) The PASCAL visual object classes (VOC) challenge. Int J Comput Vis (2009). doi:10.1007/s11263-009-0275-4
Garcia V, Debreuve E, Barlaud (2008) Fast k nearest neighbor search using GPU. CVPR workshop on computer vision on GPU
Giuseppe A, Falchi F (2010) kNN based image classification relying on local feature similarity. SISAP, pp 101–108
Griffin G, Holub A, Perona P (2007) Caltech 256 object category dataset. Technical report UCB/CSD-04-1366, California Institute of Technology
Grira N, Crucianu M, Boujemaa N (2005) Active semi supervised fuzzy clustering for image database categorization. In: 7th ACM SIGMM international workshop on multimedia information retrieval (MIR’05)
Hauptmann AG, Ngo CW, Yang J, Jiang YG (2007) Evaluating bag of visual words representations in scene classification. Multimedia information retrieval, pp 197–206
Kaski S, Kangas J, Kohonen T (1998) Bibliography of self-organizing map (SOM) papers: 1981–1997. Neural Comput Surv 1:102–350
Kimura A, Kawanishi T, Kashino K (2004) Similarity-based partial image retrieval guaranteeing same accuracy as exhaustive matching. In: Proc. international conference on multimedia and expo (ICME2004), vol 3. Taipei, Taiwan, pp 1895–1898
Li J, Wang JZ (2003) Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans Pattern Anal Mach Intell 25:1075–1088
Mejdoub M, Fonteles L, Benamar C, Antonini M (2007) Extraction d’une signature floue se basant sur la combinaison de différentes bases d’ondelettes. Traitement et analyse d’images méthodes et applications TAIMA
Mejdoub M, Fonteles L, Benamar C, Antonini M (2007) Fast algorithm for image database indexing based on lattice. In: 15th European signal processing conference, EUSIPCO. Pologne, pp 1799–1803
Mejdoub M, Fonteles L, Benamar C, Antonini M, (2007) Image retrieval system based on the beta wavelet transform. In: International conference on signal system and devices, SSD
Mejdoub M, Fonteles L, Benamar C, Antonini M (2008) Fast indexing method for image retrieval using tree-structured lattices. In: IEEE workshop on content based multimedia indexing, CBMI, London
Mejdoub M, Fonteles L, Benamar C, Antonini M (2009) Embedded lattices tree: an efficient indexing scheme for content based retrieval on image databases. J Vis Commun Image Represent 20:145–156
Mounira T, Lamrous S, Touati S (2007) Non-overlapping hierarchical index structure for similarity search. Int J Comput Sci 3(1):1544–1559
Moureaux J, Loyer P, Antonini M (1998) Low complexity indexing method for \(\textsc{Z}^n\) and \(\textsc{D}_n\) lattice quantizers. IEEE Trans Commun 46(12):1602–1609
Mouret M, Solnon C, Wolf C (2009) Classification of images based on hidden Markov models. In: 7th international workshop on content-based multimedia indexing, pp 169–174
Piro P, Anthoine S, Debreuve E, Barlaud M (2009) Sparse multiscale patches (SMP) for image categorization. In: Advances in multimedia modeling. Sophia-Antipolis, France
Tao Y, Skubic, M, Han TY, Xia, Chi X (2010) Performance Evaluation of SIFT-Based Descriptors for Object Recognition. In: Proceedings of the international multiconference of engineers and computer scientisits, IMECS
Todorovic S, Ahuja N (2006) Extracting subimages of an unknown category from a set of images. In: CVPR
Van de Sande K, Gevers T, Snoek C (2008) A comparison of color features for visual concept classification. In: CIVR, pp 141–150
Weinberger KQ, Blitzer J, Saul LK (2005) Distance metric learning for large margin nearest neighbor classification. In: NIPS
Zhang H, Berg AC, Maire M (2006) Discriminative nearest neighbor classification for visual category recognition. In: CVPR 06, IEEE computer society, Los Alamitos, CA, pp 2126–2136
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Mejdoub, M., Ben Amar, C. Classification improvement of local feature vectors over the KNN algorithm. Multimed Tools Appl 64, 197–218 (2013). https://doi.org/10.1007/s11042-011-0900-4
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DOI: https://doi.org/10.1007/s11042-011-0900-4