NDLI: Fine-tuning SIMPLE based Content Based Image Retrieval system

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Fine-tuning SIMPLE based Content Based Image Retrieval system

Content Provider	ACM Digital Library
Author	Le, Hai-Son Vu, Van-Hieu Kanishcheva, Olga Angelova, Galia
Abstract	Basically, the task of Content Based Image Retrieval (CBIR) is, given an image from an user, to find the most similar images among images in the database. It is not an easy task due to the fact that the definition of "similarity" is very different between human and computer. This difference is called "semantic gap". To filling this gap, many methods try to take into account the user feedback, i.e., running the system in cycle, at each loop to ask the feedback from users on the output, then using it to refine the searching procedure. However, focusing too much on user feedback may be harmful as it shadows the importance of feature design on this task, especially in earlier loops when less information from users are available. Many researchers investigate feature extraction for CBIR since years so it is natural to raise a question: "Is the feature design is at the top of its performance?". Recently, the study on Searching Images with MPEG-7 (& MPEG-7-like) Powered Localized dEscriptors (SIMPLE) has shown that the answer is "No". Convinced by this work but without ignoring its limitations, in this article, we aim to show that, by carefully fine-tuning a SIMPLE based system applying on deep analyses, the performance can still be improved significantly. At the end, using Gaussian distribution to detect interest regions and using Jensen-Shannon Divergence as the distance measure seems to be the key points to bootstrap the system performance. The significant relative Mean Average Precision improvements on UCID (5--7%) and UKBench (24--36%), the two well-known datasets for CBIR, support this conclusion. However, clustering the final results according to another similarity measure based on manual tags (textual features), we show that the semantic gap remains large. The integration of visual and textual features for CBIR is therefore necessary.
Starting Page	231
Ending Page	238
Page Count	8
File Format	PDF
ISBN	9781450338431
DOI	10.1145/2833258.2833273
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-12-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Content based image retrieval (cbir) Integration Textual Tag/keyword Gaussian distribution Jensen-shannon divergence Fine-tuning Optimization Visual Searching images with mpeg-7 (& mpeg-7-like) powered localized descriptors (simple)
Content Type	Text
Resource Type	Article

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