Shopbot: An Image Based Search Application for E-Commerce Domain

For the past few years, e-commerce has changed the way people buy and sell products. People use this business model to do business over the Internet. In this domain, Human-Computer Interaction has been gaining momentum. Lately, there has been an upsurge in agent based applications in the form of intelligent personal assistants (also known as Chatbots) which make it easier for users to interact with digital services via a conversation, in the same way we talk to humans. In e- commerce, these assistants offer mainly text-based or speech based search capabilities. They can handle search for most products, but cannot handle search that is based on product features, for instance color or pattern of a T-shirt. Most of the times, it is difficult for users to define these characteristics while searching for a product. Furthermore, a growing number of consumers rely on social media to make a purchasing decision. They try to find out what is trending right now and look for similar items. This brings us the need of a virtual shopping assistant or a shopbot which recommends products based on an image of the product provided by a user. It will be designed to provide relevant responses to the user queries by performing image recognition. This report explains the proposed approach along with the implementation for the virtual shopping assistant

Shopbot: An Image Based Search Application for E-Commerce Domain

For the past few years, e-commerce has changed the way people buy and sell products. People use this business model to do business over the Internet. In this domain, Human-Computer Interaction has been gaining momentum. Lately, there has been an upsurge in agent based applications in the form of intelligent personal assistants (also known as Chatbots) which make it easier for users to interact with digital services via a conversation, in the same way we talk to humans. In e- commerce, these assistants offer mainly text-based or speech based search capabilities. They can handle search for most products, but cannot handle search that is based on product features, for instance color or pattern of a T-shirt. Most of the times, it is difficult for users to define these characteristics while searching for a product. Furthermore, a growing number of consumers rely on social media to make a purchasing decision. They try to find out what is trending right now and look for similar items. This brings us the need of a virtual shopping assistant or a shopbot which recommends products based on an image of the product provided by a user. It will be designed to provide relevant responses to the user queries by performing image recognition. This report explains the proposed approach along with the implementation for the virtual shopping assistant

Visual thesaurus for color image retrieval using SOM.

Yip King-Fung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 84-89).Abstracts in English and Chinese.Abstract --- p.i論文摘要 --- p.iiiTable of Contents --- p.ivList of Abbreviations --- p.viAcknowledgements --- p.viiChapter 1. --- Introduction --- p.1Chapter 1.1. --- Background --- p.1Chapter 1.2. --- Motivation --- p.3Chapter 1.3. --- Thesis Organization --- p.4Chapter 2. --- A Survey of Content-based Image Retrieval --- p.5Chapter 2.1. --- Text-based Image Retrieval --- p.5Chapter 2.2. --- Content-Based Image Retrieval --- p.7Chapter 2.2.1. --- Content-Based Image Retrieval Systems --- p.7Chapter 2.2.2. --- Query Methods --- p.9Chapter 2.2.3. --- Image Features --- p.11Chapter 2.2.4. --- Summary --- p.16Chapter 3. --- Visual Thesaurus using SOM --- p.17Chapter 3.1. --- Algorithm --- p.17Chapter 3.1.1. --- Image Representation --- p.17Chapter 3.1.2. --- Self-Organizing Map --- p.21Chapter 3.2. --- Preliminary Experiment --- p.27Chapter 3.2.1. --- Feature differences --- p.27Chapter 3.2.2. --- Labeling differences --- p.30Chapter 4. --- Experiment --- p.33Chapter 4.1. --- Subjects --- p.33Chapter 4.2. --- Apparatus --- p.33Chapter 4.2.1. --- Systems --- p.33Chapter 4.2.2. --- Test Databases --- p.33Chapter 4.3. --- Procedure --- p.34Chapter 4.3.1. --- Description --- p.35Chapter 4.3.2. --- SOM (text) --- p.36Chapter 4.3.3. --- SOM (image) --- p.38Chapter 4.3.4. --- QBE (text) --- p.40Chapter 4.3.5. --- QBE (image) --- p.42Chapter 4.3.6. --- Questionnaire --- p.44Chapter 4.3.7. --- Experiment Flow --- p.45Chapter 4.4. --- Results --- p.46Chapter 4.5. --- Discussion --- p.51Chapter 5. --- Quantizing Color Histogram --- p.55Chapter 5.1. --- Algorithm --- p.56Chapter 5.1.1. --- Codebook Generation Phrase --- p.57Chapter 5.1.2. --- Histogram Generation Phrase --- p.66Chapter 5.2. --- Experiment --- p.67Chapter 5.2.1. --- Test Database --- p.67Chapter 5.2.2. --- Evaluation Methods --- p.67Chapter 5.2.3. --- Results and Discussion --- p.69Chapter 5.2.4. --- Summary --- p.74Chapter 6. --- Relevance Feedback --- p.75Chapter 6.1. --- Relevance Feedback in Text Information Retrieval --- p.75Chapter 6.2. --- Relevance Feedback in Multimedia Information Retrieval --- p.76Chapter 6.3. --- Relevance Feedback in Visual Thesaurus --- p.76Chapter 7. --- Conclusions --- p.80Chapter 7.1. --- Applications --- p.81Chapter 7.2. --- Future Directions --- p.81Chapter 7.2.1. --- SOM Generation --- p.81Chapter 7.2.2. --- Hybrid Architecture --- p.82References --- p.8

Image Browsing for Infomediaries

Information intermediary or infomediary is likely to claim a major segment of the revenue of e-commerce transactions. Infomediaries are e-commerce companies leveraging the Internet to unite buyers and suppliers in a single efficient virtual marketplace to facilitate the consummation of a transaction. Most of the search facilities in the infomediaries are text- and navigation-based, fo