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Adaptive Multimedial Retrieval: Retrieval, User, and Semantics: 5th International Workshop, AMR 2007, Paris, France, July 5-6, 2007 Revised Selected Papers
05 Jul 2007-
About: The article was published on 2007-07-05 and is currently open access. It has received 2 citations till now. The article focuses on the topics: Semantics.
Citations
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01 Dec 2015
TL;DR: This paper proposes a novel automated planogram compliance checking method for retail chains without requiring product template images for modeling or training by means of unsupervised recurring pattern detection and a divide-conquer strategy.
Abstract: This paper proposes a novel automated planogram compliance checking method for retail chains without requiring product template images for modeling or training. Product layout information is extracted from one single input image by means of unsupervised recurring pattern detection and matched via graph matching with the expected product layout specified by a planogram. To improve the efficiency, a divide-conquer strategy is employed. Specifically, the input image is divided into several regions based on the planogram. Recurring patterns are detected in each region respectively and then merged together to estimate product layout information. Experimental results on real data from a supermarket chain have verified the effectiveness and efficiency of the proposed method.
14 citations
Cites methods from "Adaptive Multimedial Retrieval: Ret..."
...[1] presented a product detection system from input images by matching with existing templates using scale-invariant feature transform (SIFT) vector....
[...]
15 Dec 2011
TL;DR: A novel dance posture based annotation model by combining features using Multiple Kernel Learning (MKL) and a novel feature representation which represents the local texture properties of the image is proposed.
Abstract: We present a novel dance posture based annotation model by combining features using Multiple Kernel Learning (MKL). We have proposed a novel feature representation which represents the local texture properties of the image. The annotation model is defined in the direct a cyclic graph structure using the binary MKL algorithm. The bag-of-words model is applied for image representation. The experiments have been performed on the image collection belonging to two Indian classical dances (Bharatnatyam and Odissi). The annotation model has been tested using SIFT and the proposed feature individually and by optimally combining both the features. The experiments have shown promising results.
7 citations
Cites background from "Adaptive Multimedial Retrieval: Ret..."
...Some of the recent works have attempted segmentation based annotation model [3], [4], [5]....
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References
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01 Dec 2015
TL;DR: This paper proposes a novel automated planogram compliance checking method for retail chains without requiring product template images for modeling or training by means of unsupervised recurring pattern detection and a divide-conquer strategy.
Abstract: This paper proposes a novel automated planogram compliance checking method for retail chains without requiring product template images for modeling or training. Product layout information is extracted from one single input image by means of unsupervised recurring pattern detection and matched via graph matching with the expected product layout specified by a planogram. To improve the efficiency, a divide-conquer strategy is employed. Specifically, the input image is divided into several regions based on the planogram. Recurring patterns are detected in each region respectively and then merged together to estimate product layout information. Experimental results on real data from a supermarket chain have verified the effectiveness and efficiency of the proposed method.
14 citations
15 Dec 2011
TL;DR: A novel dance posture based annotation model by combining features using Multiple Kernel Learning (MKL) and a novel feature representation which represents the local texture properties of the image is proposed.
Abstract: We present a novel dance posture based annotation model by combining features using Multiple Kernel Learning (MKL). We have proposed a novel feature representation which represents the local texture properties of the image. The annotation model is defined in the direct a cyclic graph structure using the binary MKL algorithm. The bag-of-words model is applied for image representation. The experiments have been performed on the image collection belonging to two Indian classical dances (Bharatnatyam and Odissi). The annotation model has been tested using SIFT and the proposed feature individually and by optimally combining both the features. The experiments have shown promising results.
7 citations