-
Yi, K.: ¬A semantic similarity approach to predicting Library of Congress subject headings for social tags (2010)
0.00
0.0022070347 = product of:
0.008828139 = sum of:
0.008828139 = product of:
0.026484415 = sum of:
0.026484415 = weight(_text_:k in 3707) [ClassicSimilarity], result of:
0.026484415 = score(doc=3707,freq=2.0), product of:
0.13429943 = queryWeight, product of:
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.037621226 = queryNorm
0.19720423 = fieldWeight in 3707, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.0390625 = fieldNorm(doc=3707)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
-
Matthews, B.; Jones, C.; Puzon, B.; Moon, J.; Tudhope, D.; Golub, K.; Nielsen, M.L.: ¬An evaluation of enhancing social tagging with a knowledge organization system (2010)
0.00
0.0022070347 = product of:
0.008828139 = sum of:
0.008828139 = product of:
0.026484415 = sum of:
0.026484415 = weight(_text_:k in 4171) [ClassicSimilarity], result of:
0.026484415 = score(doc=4171,freq=2.0), product of:
0.13429943 = queryWeight, product of:
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.037621226 = queryNorm
0.19720423 = fieldWeight in 4171, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.0390625 = fieldNorm(doc=4171)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
-
Sun, A.; Bhowmick, S.S.; Nguyen, K.T.N.; Bai, G.: Tag-based social image retrieval : an empirical evaluation (2011)
0.00
0.0022070347 = product of:
0.008828139 = sum of:
0.008828139 = product of:
0.026484415 = sum of:
0.026484415 = weight(_text_:k in 4938) [ClassicSimilarity], result of:
0.026484415 = score(doc=4938,freq=2.0), product of:
0.13429943 = queryWeight, product of:
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.037621226 = queryNorm
0.19720423 = fieldWeight in 4938, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.0390625 = fieldNorm(doc=4938)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Abstract
- Tags associated with social images are valuable information source for superior image search and retrieval experiences. Although various heuristics are valuable to boost tag-based search for images, there is a lack of general framework to study the impact of these heuristics. Specifically, the task of ranking images matching a given tag query based on their associated tags in descending order of relevance has not been well studied. In this article, we take the first step to propose a generic, flexible, and extensible framework for this task and exploit it for a systematic and comprehensive empirical evaluation of various methods for ranking images. To this end, we identified five orthogonal dimensions to quantify the matching score between a tagged image and a tag query. These five dimensions are: (i) tag relatedness to measure the degree of effectiveness of a tag describing the tagged image; (ii) tag discrimination to quantify the degree of discrimination of a tag with respect to the entire tagged image collection; (iii) tag length normalization analogous to document length normalization in web search; (iv) tag-query matching model for the matching score computation between an image tag and a query tag; and (v) query model for tag query rewriting. For each dimension, we identify a few implementations and evaluate their impact on NUS-WIDE dataset, the largest human-annotated dataset consisting of more than 269K tagged images from Flickr. We evaluated 81 single-tag queries and 443 multi-tag queries over 288 search methods and systematically compare their performances using standard metrics including Precision at top-K, Mean Average Precision (MAP), Recall, and Normalized Discounted Cumulative Gain (NDCG).
-
Yoon, K.: Conceptual syntagmatic associations in user tagging (2012)
0.00
0.0022070347 = product of:
0.008828139 = sum of:
0.008828139 = product of:
0.026484415 = sum of:
0.026484415 = weight(_text_:k in 240) [ClassicSimilarity], result of:
0.026484415 = score(doc=240,freq=2.0), product of:
0.13429943 = queryWeight, product of:
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.037621226 = queryNorm
0.19720423 = fieldWeight in 240, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.0390625 = fieldNorm(doc=240)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
-
Knautz, K.; Stock, W.G.: Collective indexing of emotions in videos (2011)
0.00
0.0022070347 = product of:
0.008828139 = sum of:
0.008828139 = product of:
0.026484415 = sum of:
0.026484415 = weight(_text_:k in 295) [ClassicSimilarity], result of:
0.026484415 = score(doc=295,freq=2.0), product of:
0.13429943 = queryWeight, product of:
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.037621226 = queryNorm
0.19720423 = fieldWeight in 295, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.0390625 = fieldNorm(doc=295)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
-
Weiand, K.; Hartl, A.; Hausmann, S.; Furche, T.; Bry, F.: Keyword-based search over semantic data (2012)
0.00
0.0022070347 = product of:
0.008828139 = sum of:
0.008828139 = product of:
0.026484415 = sum of:
0.026484415 = weight(_text_:k in 432) [ClassicSimilarity], result of:
0.026484415 = score(doc=432,freq=2.0), product of:
0.13429943 = queryWeight, product of:
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.037621226 = queryNorm
0.19720423 = fieldWeight in 432, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.0390625 = fieldNorm(doc=432)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
