Query/Task Satisfaction and Grid-based Evaluation Metrics Under Different Image Search Intents

2020 ◽  
Author(s):  
Kosetsu Tsukuda ◽  
Masataka Goto
Keyword(s):  
Evaluation Metrics ◽  
Image Search ◽  
Task Satisfaction ◽  
Grid Based

scholarly journals Grid-based Evaluation Metrics for Web Image Search

2019 ◽  
Author(s):  
Xiaohui Xie ◽  
Jiaxin Mao ◽  
Yiqun Liu ◽  
Maarten de Rijke ◽  
Yunqiu Shao ◽  
...  
Keyword(s):  
Evaluation Metrics ◽  
Image Search ◽  
Grid Based

scholarly journals Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics

2013 ◽  
Vol 47 ◽  
pp. 853-899 ◽  
Author(s):  
M. Hodosh ◽  
P. Young ◽  
J. Hockenmaier
Keyword(s):  
Large Scale ◽  
Image Annotation ◽  
Image Understanding ◽  
Evaluation Metrics ◽  
Semantic Features ◽  
Image Search ◽  
Image Description ◽  
Query Image ◽  
Relevance Judgments ◽  
Single Response

The ability to associate images with natural language sentences that describe what is depicted in them is a hallmark of image understanding, and a prerequisite for applications such as sentence-based image search. In analogy to image search, we propose to frame sentence-based image annotation as the task of ranking a given pool of captions. We introduce a new benchmark collection for sentence-based image description and search, consisting of 8,000 images that are each paired with five different captions which provide clear descriptions of the salient entities and events. We introduce a number of systems that perform quite well on this task, even though they are only based on features that can be obtained with minimal supervision. Our results clearly indicate the importance of training on multiple captions per image, and of capturing syntactic (word order-based) and semantic features of these captions. We also perform an in-depth comparison of human and automatic evaluation metrics for this task, and propose strategies for collecting human judgments cheaply and on a very large scale, allowing us to augment our collection with additional relevance judgments of which captions describe which image. Our analysis shows that metrics that consider the ranked list of results for each query image or sentence are significantly more robust than metrics that are based on a single response per query. Moreover, our study suggests that the evaluation of ranking-based image description systems may be fully automated.

Customized Concept Learning in Image Search from Photo Sharing Websites

2012 ◽  
Vol 2 (2) ◽  
pp. 114-115
Author(s):  
Ch. priyanka Ch. priyanka ◽  
◽  
Dr P.Govardhan Dr P.Govardhan
Keyword(s):  
Concept Learning ◽  
Image Search ◽  
Photo Sharing

Pencarian File Gambar Berdasarkan Dominasi Warna

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Vina Chovan Epifania ◽  
Eko Sediyono
Keyword(s):  
Digital Image ◽  
Retrieval System ◽  
Color Image ◽  
Color Space ◽  
Image Data ◽  
Image Search ◽  
Test Results ◽  
Application Form ◽  
Space Model ◽  
Image File

Abstract. Image File Searching Based on Color Domination. One characteristic of an image that can be used in image searching process is the composition of the colors. Color is a trait that is easily seen by man in the picture. The use of color as a searching parameter can provide a solution in an easier searching for images stored in computer memory. Color images have RGB values that can be computed and converted into HSL color space model. Use of HSL images model is very easy because it can be calculated using a percent, so that in each pixel of the image can be grouped and named, this can give a dominant values of the colors contained in one image. By obtaining these values, the image search can be done quickly just by using these values to a retrieval system image file. This article discusses the use of the HSL color space model to facilitate the searching for a digital image in the digital image data warehouse. From the test results of the application form, a searching is faster by using the colors specified by the user. Obstacles encountered were still searching with a choice of 15 basic colors available, with a limit of 33% dominance of the color image search was not found. This is due to the dominant color in each image has the most dominant value below 33%.   Keywords: RGB, HSL, image searching Abstrak. Salah satu ciri gambar yang dapat dipergunakan dalam proses pencarian gambar adalah komposisi warna. Warna adalah ciri yang mudah dilihat oleh manusia dalam citra gambar. Penggunaan warna sebagai parameter pencarian dapat memberikan solusi dalam memudahkan pencarian gambar yang tersimpan dalam memori komputer. Warna gambar memiliki nilai RGB yang dapat dihitung dan dikonversi ke dalam model HSL color space. Penggunaan model gambar HSL sangat mudah karena dapat dihitung dengan menggunakan persen, sehingga dalam setiap piksel gambar dapat dikelompokan dan diberi nama, hal ini dapat memberikan suatu nilai dominan dari warna yang terdapat dalam satu gambar. Dengan diperolehnya nilai tersebut, pencarian gambar dapat dilakukan dengan cepat hanya dengan menggunakan nilai tersebut pada sistem pencarian file gambar. Artikel ini membahas tentang penggunaan model HSL color space untuk mempermudah pencarian suatu gambar digital didalam gudang data gambar digital. Dari hasil uji aplikasi yang sudah dibuat, diperoleh pencarian yang lebih cepat dengan menggunakan pilihan warna yang ditentukan sendiri oleh pengguna. Kendala yang masih dijumpai adalah pencarian dengan pilihan 15 warna dasar yang tersedia, dengan batas dominasi warna 33% tidak ditemukan gambar yang dicari. Hal ini disebabkan warna dominan disetiap gambar kebanyakan memiliki nilai dominan di bawah 33%. Kata Kunci: RGB, HSL, pencarian gambar

The Effects of Mood on Task Performance and Task Satisfaction

1997 ◽  
Vol 2 (4) ◽  
pp. 127-132
Author(s):  
Doreen Cicchetti ◽  
Kathleen Moyer ◽  
Jeffrey P. Nicholas
Keyword(s):  
Task Performance ◽  
Task Satisfaction ◽  
And Task
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