An Image Retrieval System using Impressional Words Based on Individual Preference

Nambo, Hidetaka; Okamine, Tadashi; Kimura, Hitoshi; Nakazawa, Makoto; Hattori, Shimmi

doi:10.1541/ieejeiss.127.1937

Cited by 1 publication

(1 citation statement)

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“…Next, existing Kansei information retrieval systems [11], [12], [20] have a common approach to information retrieval. First, these systems are built on server-client networks.…”

Section: Related Workmentioning

confidence: 99%

Folksonomical P2P File Sharing Networks Using Vectorized KANSEI Information as Search Tags

Ohnishi

Yoshida

Oie

2009

IEICE Trans. Inf. & Syst.

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SUMMARYWe present the concept of folksonomical peer-to-peer (P2P) file sharing networks that allow participants (peers) to freely assign structured search tags to files. These networks are similar to folksonomies in the present Web from the point of view that users assign search tags to information distributed over a network. As a concrete example, we consider an unstructured P2P network using vectorized Kansei (human sensitivity) information as structured search tags for file search. Vectorized Kansei information as search tags indicates what participants feel about their files and is assigned by the participant to each of their files. A search query also has the same form of search tags and indicates what participants want to feel about files that they will eventually obtain. A method that enables file search using vectorized Kansei information is the Kansei query-forwarding method, which probabilistically propagates a search query to peers that are likely to hold more files having search tags that are similar to the query. The similarity between the search query and the search tags is measured in terms of their dot product. The simulation experiments examine if the Kansei query-forwarding method can provide equal search performance for all peers in a network in which only the Kansei information and the tendency with respect to file collection are different among all of the peers. The simulation results show that the Kansei query forwarding method and a random-walk-based query forwarding method, for comparison, work effectively in different situations and are complementary. Furthermore, the Kansei query forwarding method is shown, through simulations, to be superior to or equal to the random-walk based one in terms of search speed.

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“…Next, existing Kansei information retrieval systems [11], [12], [20] have a common approach to information retrieval. First, these systems are built on server-client networks.…”

Section: Related Workmentioning

confidence: 99%